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Title:

Automatic Generation of Exams in R

Description:

Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for large-scale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R c...

Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for large-scale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R code for data generation and LaTeX code for problem and solution description) and the master file is a LaTeX document with some additional control commands. This paper gives an overview of the main design aims and principles as well as strategies for adaptation and extension. Hands-on illustrations---based on example exercises and control files provided in the package---are presented to get new users started easily. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2009-02-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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050 General serial publications *(computed)*

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http://www.jstatsoft.org/v29/i10/paper

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Title:

On maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions

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Maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions involves inverting the ratio $$R_\nu = I_{\nu +1} / I_\nu $$
Rν=Iν+1/Iν
of modified Bessel functions and computational methods are required to invert these functions using approximative or iterative algorith...

Maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions involves inverting the ratio $$R_\nu = I_{\nu +1} / I_\nu $$
Rν=Iν+1/Iν
of modified Bessel functions and computational methods are required to invert these functions using approximative or iterative algorithms. In this paper we use Amos-type bounds for $$R_\nu $$
Rν
to deduce sharper bounds for the inverse function, determine the approximation error of these bounds, and use these to propose a new approximation for which the error tends to zero when the inverse of $$R_\nu $$
Rν
is evaluated at values tending to $$1$$
1
(from the left). We show that previously introduced rational bounds for $$R_\nu $$
Rν
which are invertible using quadratic equations cannot be used to improve these bounds. Minimize

Publisher:

Springer Berlin Heidelberg

Year of Publication:

2014-10-01

Source:

Computational Statistics, 2014-10-01, Volume 29, pp 945-957

Computational Statistics, 2014-10-01, Volume 29, pp 945-957 Minimize

Document Type:

Original Paper

Language:

En

Subjects:

Maximum likelihood ; Modified Bessel function ratio ; Numerical approximation ; von Mises–Fisher distribution

Maximum likelihood ; Modified Bessel function ratio ; Numerical approximation ; von Mises–Fisher distribution Minimize

Rights:

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. Minimize

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Title:

Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects

Description:

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity Minimize

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article

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Title:

topicmodels: An R Package for Fitting Topic Models

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Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure f...

Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors. Minimize

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Title:

FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters

Description:

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can ...

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined. Minimize

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Title:

Automatic Generation of Exams in R

Description:

Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for large-scale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R c...

Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for large-scale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R code for data generation and LaTeX code for problem and solution description) and the master file is a LaTeX document with some additional control commands. This paper gives an overview of the main design aims and principles as well as strategies for adaptation and extension. Hands-on illustrations---based on example exercises and control files provided in the package---are presented to get new users started easily. Minimize

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Title:

Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned

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Beta regression – an increasingly popular approach for modeling rates and proportions – is extended in various directions: (a) bias correction/reduction of the maximum likelihood estimator, (b) beta regression tree models by means of recursive partitioning, (c) latent class beta regression by means of finite mixture models. All three extensions ...

Beta regression – an increasingly popular approach for modeling rates and proportions – is extended in various directions: (a) bias correction/reduction of the maximum likelihood estimator, (b) beta regression tree models by means of recursive partitioning, (c) latent class beta regression by means of finite mixture models. All three extensions may be of importance for enhancing the beta regression toolbox in practice to provide more reliable inference and capture both observed and unobserved/latent heterogeneity in the data. Using the analogy of Smithson and Verkuilen (2006), these extensions make beta regression not only “a better lemon squeezer” (compared to classical least squares regression) but a full-fledged modern juicer offering lemon-based drinks: shaken and stirred (bias correction and reduction), mixed (finite mixture model), or partitioned (tree model). All three extensions are provided in the R package betareg (at least 2.4-0), building on generic algorithms and implementations for bias correction/reduction, model-based recursive partioning, and finite mixture models, respectively. Specifically, the new functions betatree() and betamix() reuse the object-oriented flexible implementation from the R packages party and flexmix, respectively. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2012-05-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

beta regression ; bias correction ; bias reduction ; recursive partitioning ; finite mixture ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC...

beta regression ; bias correction ; bias reduction ; recursive partitioning ; finite mixture ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

DDC:

310 Collections of general statistics *(computed)*

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http://www.jstatsoft.org/v48/i11/paper

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Title:

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Author:

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2005-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v14/i15/paper

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Title:

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

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Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The...

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules. Minimize

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License GPL (> = 2)

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Description FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models,generalized linear models and modelbased clu...

Description FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models,generalized linear models and modelbased clustering. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-24

Source:

http://cran.at.r-project.org/web/packages/flexmix/flexmix.pdf

http://cran.at.r-project.org/web/packages/flexmix/flexmix.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Depends R (> = 2.15.0 ; lattice Imports grid ; methods ; modeltools (> = 0.2-16 ; stats ; stats4 Suggests MASS ; codetools ; diptest ; ellipse ; gclus ; lme4 ; mgcv (>= 1.6-1 ; mlbench ; mlogit ; multcomp ; mvtnorm ; nnet ; survival

Depends R (> = 2.15.0 ; lattice Imports grid ; methods ; modeltools (> = 0.2-16 ; stats ; stats4 Suggests MASS ; codetools ; diptest ; ellipse ; gclus ; lme4 ; mgcv (>= 1.6-1 ; mlbench ; mlogit ; multcomp ; mvtnorm ; nnet ; survival Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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