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Description: A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.
A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
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Size: 38711 |
Author: yupenghui |
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Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Package source: sbgcop_0.95.tar.gz
MacOS X binary: sbgcop_0.95.tgz
Windows binary: sbgcop_0.95.zip
Reference manual: sbgcop.pdf
Platform: |
Size: 5273 |
Author: 陈远 |
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Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Windows binary: sbgcop_0.95.zip
Platform: |
Size: 40754 |
Author: 陈远 |
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Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Reference manual: sbgcop.pdf
Platform: |
Size: 94209 |
Author: 陈远 |
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Description: Univariate local or global optimization
matlab最优化工具包，可以进行最优化点的搜寻。
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Size: 13168 |
Author: ggyyree |
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Description: A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.
A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/. -A series of. C and. M files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.A set of commands is provided . For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
Platform: |
Size: 37888 |
Author: yupenghui |
Hits:
Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Package source: sbgcop_0.95.tar.gz
MacOS X binary: sbgcop_0.95.tgz
Windows binary: sbgcop_0.95.zip
Reference manual: sbgcop.pdf
Platform: |
Size: 5120 |
Author: cy |
Hits:
Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Windows binary: sbgcop_0.95.zip
Platform: |
Size: 39936 |
Author: cy |
Hits:
Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Reference manual: sbgcop.pdf
Platform: |
Size: 94208 |
Author: cy |
Hits:
Description: Univariate local or global optimization
matlab最优化工具包，可以进行最优化点的搜寻。-Univariate local or global optimizationmatlab optimization tool kit can be carried out to optimize the search point.
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Size: 13312 |
Author: ggyyree |
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Description: the text file QMLE contains the quasi maximum
likelyhood estimating procedure and performing Information Matrix test
for a univariate GARCH(1,1) model
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Size: 3072 |
Author: 萧建 |
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Description: function [U,V,num_it]=fcm(U0,X)
％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.
％ Hathaway on June 21, 1994.) The fuzzification constant
％ m = 2, and the stopping criterion for successive partitions is epsilon =??????.
％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********
％ Purpose:The function fcm attempts to find a useful clustering of the
％ objects represented by the object data in X using the initial partition in U0. -function [U, V, num_it] = fcm (U0, X) MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J. Hathaway on June 21, 1994.) The fuzzification constant m = 2, and the stopping criterion for successive partitions is epsilon =??????.******* Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug******** Purpose: The function fcm attempts to find a useful clustering of the objects represented by the object data in X using the initial partition in U0.
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Size: 1024 |
Author: download99 |
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Description: 基于单变量的广义自适应预测控制程序，可应用于单输入单输出系统-Univariate broad-based adaptive predictive control procedures, can be applied to single-input single-output system
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Size: 1024 |
Author: 杨怀申 |
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Description: A toolkit for Gaussian mixtures.
flexible tools for:
Generating univariate, multivariate, or mixtures of gaussians
Interactive viewing tools allows viewing of multidimensional data and models. Initialize models, add and remove dimensions or clusters and inspect the fit in real-time.
Also includes tools to subset the data using model-based (pseudo-)metrics.-A toolkit for Gaussian mixtures.
flexible tools for:
Generating univariate, multivariate, or mixtures of gaussians
Interactive viewing tools allows viewing of multidimensional data and models. Initialize models, add and remove dimensions or clusters and inspect the fit in real-time.
Also includes tools to subset the data using model-based (pseudo-)metrics.
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Size: 63488 |
Author: tra ba huy |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
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Size: 109568 |
Author: jon |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
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Size: 117760 |
Author: jon |
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Description: genetic programing for free cash forecasting in univariate setting
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Size: 8547328 |
Author: tangkewei |
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Description: univariate normal distribution
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Size: 35840 |
Author: jorgehas |
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Description: Univariate Moran's I 指数计算过程脚本，运用ArcGIS自带的arcpy工具包(Univariate Moran's I calculation script using ArcPy tool in ArcGIS Desktop software)
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Size: 2048 |
Author: Boorn
|
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Description: This code allows to calculate the recursive kalman filter and to estimate kalman filter. The files are: 1) Calculate recursive univariate kalman filter 2) Calculate recurisve multivariate kalman filter 3) Estimate kalman filter parameters
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Size: 4381 |
Author: franciscososasotomayor123 |
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