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Showing results 1 to 10 of 1,015.


Function delta_GMM [LambertW v0.6.4]
keywords
optimize
title
Estimate delta
description
This function minimizes the Euclidean distance between the sample kurtosis of the back-transformed data $W_{\delta}(\boldsymbol z)$ and a user-specified target kurtosis as a function of $\delta$ (see References). Only an iterative application of this function will give a good estimate of $\delta$ (see IGMM).
Function gamma_GMM [LambertW v0.6.4]
keywords
optimize
title
Estimate gamma
description
This function minimizes the Euclidean distance between the theoretical skewness of a skewed Lambert W x Gaussian random variable and the sample skewness of the back-transformed data $W_{\gamma}(\boldsymbol z)$ as a function of $\gamma$ (see References). Only an interative application of this function will give a good estimate of $\gamma$ (see IGMM).
Function MLE_LambertW [LambertW v0.6.4]
keywords
optimize
title
Maximum Likelihood Estimation for Lambert W$ \times$ F distributions
description
Maximum Likelihood Estimation (MLE) for Lambert W $\times F$ distributions computes $\widehat{\theta}_{MLE}$. For type = "s", the skewness parameter $\gamma$ is estimated and $\delta = 0$ is held fixed; for type = "h" the one-dimensional $\delta$ is estimated and $\gamma = 0$ is held fixed; and for type = "hh" the 2-dimensional $\delta$ is estimated and $\gamma = 0$ is held fixed. By default $\alpha = 1$ is fixed for any type. If you want to also estimate $\alpha$ (for type = "h" or "hh") set theta.fixed = list().
Function genopt [BurStMisc v1.1]
keywords
optimize
title
Genetic Optimization
description
Approximately minimizes the value of a function using a simple heuristic optimizer that uses a combination of genetic and simulated annealing optimization.
Function genopt.control [BurStMisc v1.1]
keywords
optimize
title
Control parameters for genopt
description
Returns a list suitable as the control argument of the genopt function.
Function summary.genopt [BurStMisc v1.1]
keywords
optimize
title
Summary of genopt object
description
The call, best solution and summary of objectives in the final population.
Function cmaes [cmaesr v1.0.3]
keywords
optimize
title
Covariance-Matrix-Adaptation
description
Performs non-linear, non-convex optimization by means of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES).
Function Rcplex [Rcplex v0.3-3]
keywords
optimize
title
Solve optimization problem with CPLEX
description
Interface to CPLEX solvers for linear quadratic and (linear or quadratic) mixed-integer programs. The general statement of the problem is $$\min \frac{1}{2}x'Qx + c'x$$ $$\mathrm{s.t} Ax \leq b$$ $$lb \leq x \leq ub$$ If Q==NULL then the problem is linear, if any value of the vtype argument is "B" or "I" then the problem is a mixed-integer program. The control argument is used to set CPLEX's many parameters. See details. The objsense determines if the problem is a maximization or minimization problem. The sense argument is used to set the constraint directions.
Function Rcplex_solve_QCP [Rcplex v0.3-3]
keywords
optimize
title
Solve quadratically constrained optimization problem with CPLEX
description
Interface to CPLEX solvers for quadratically constrained linear, quadratic, and mixed-integer programs. The general statement of the problem is $$\min \frac{1}{2}x'Qx + c'x$$ $$\mathrm{s.t} Ax \leq b$$ $$\mathrm{and} a_i'x + x'Q_ix \leq r_i for i=1,\ldots,q$$ $$lb \leq x \leq ub$$ If Q==NULL then the problem is linear, if any value of the vtype argument is "B" or "I" then the problem is a mixed-integer program. The control argument is used to set CPLEX's many parameters. See details. The objsense determines if the problem is a maximization or minimization problem. The sense argument is used to set the constraint directions.
Function kern_smooth [npbr v1.6]
keywords
optimize
title
Frontier estimation via kernel smoothing
description
The function kern_smooth implements two frontier estimators based on kernel smoothing techniques. One is from Noh (2014) and the other is from Parmeter and Racine (2013).