Sat, 25 Feb 2012

Package lifecontingencies updated to version 0.1.1 with previous version 0.1 dated 2012-01-02

Title: Package to perform actuarial mathematics for life contingencies insurances
Description: Financial and actuarial functions to evaluate life contingencies.
Author: Giorgio A. Spedicato
Maintainer: Giorgio A. Spedicato

Diff between lifecontingencies versions 0.1 dated 2012-01-02 and 0.1.1 dated 2012-02-25

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Package endorse updated to version 1.2 with previous version 1.1 dated 2012-02-13

Title: R Package for Analyzing Endorsement Experiments
Description: This R package implements the statistical model proposed by Bullock, Imai, and Shapiro (2011; Political Analysis) to analyze endorsement experiments. Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions. This methodology is helpful when measuring support for socially sensitive political actors such as militant groups. The model is fitted with the Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Author: Yuki Shiraito , Kosuke Imai
Maintainer: Yuki Shiraito

Diff between endorse versions 1.1 dated 2012-02-13 and 1.2 dated 2012-02-25

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Package RHmm updated to version 2.0.0 with previous version 1.5.5 dated 2011-12-27

Title: Hidden Markov Models simulations and estimations
Description: Discrete, univariate or multivariate gaussian, mixture of univariate or multivariate gaussian HMM functions for simulation and estimation.
Author: Ollivier TARAMASCO, Sebastian Bauer
Maintainer: Ollivier TARAMASCO , Sebastian Bauer

Diff between RHmm versions 1.5.5 dated 2011-12-27 and 2.0.0 dated 2012-02-25

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Package dcmle updated to version 0.1-5 with previous version 0.1-4 dated 2012-02-08

Title: Hierarchical Models Made Easy with Data Cloning
Description: S4 classes around infrastructure provided by the dclone package to make package development with data cloning for hierarchical models easy as a breeze.
Author: Peter Solymos
Maintainer: Peter Solymos

Diff between dcmle versions 0.1-4 dated 2012-02-08 and 0.1-5 dated 2012-02-25

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Package haplo.stats updated to version 1.5.5 with previous version 1.5.2 dated 2011-12-13

Title: Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous
Description: A suite of R routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em, haplo.glm, haplo.score, haplo.power, and seqhap. Copyright: 2003 Mayo Foundation for Medical Education and Research.
Author: Sinnwell JP, Schaid DJ
Maintainer: Jason P. Sinnwell

Diff between haplo.stats versions 1.5.2 dated 2011-12-13 and 1.5.5 dated 2012-02-25

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Package granovaGG updated to version 1.1 with previous version 1.0 dated 2011-09-04

Title: Graphical Analysis of Variance Using ggplot2
Description: This collection of functions in granovaGG provides what we call elemental graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use ggplot2, Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w (a graphic for one way anova); two other functions (granovagg.ds and granovagg.contr) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w, which entails dynamic displays of data, is not currently part of granovaGG.) The granovaGG functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds and granovagg.contr provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w and granovagg.ds several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions.
Author: Brian A. Danielak , Robert M. Pruzek , with contributions from: William E. J. Doane , James E. Helmreich , Jason Bryer
Maintainer: Brian A. Danielak

Diff between granovaGG versions 1.0 dated 2011-09-04 and 1.1 dated 2012-02-25

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