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Showing results 1 to 10 of 1,004.


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 grback [optextras v2016-8.8]
keywords
optimize
title
Backward difference numerical gradient approximation.
description
grback computes the backward difference approximation to the gradient of user function userfn.
Function hesschk [optextras v2016-8.8]
keywords
optimize
title
Run tests, where possible, on user objective function and (optionally) gradient and hessian
description
hesschk checks a user-provided R function, ffn.
Function grcentral [optextras v2016-8.8]
keywords
optimize
title
Central difference numerical gradient approximation.
description
grcentral computes the central difference approximation to the gradient of user function userfn.