Packaged: 2013-02-22 14:03:31 UTC; vanderleidebastiani

Title: Sabermetrics Functions For Baseball Analytics
Description: A collection of baseball analytics functions for sabermetrics purposes. Among these functions include popular metrics such as OBP, wOBA, runs created functions as well as field-independent pitching metrics.
Packaged: 2015-02-06 21:37:56 UTC; pxenopoulos

Title: Sabermetrics Functions for Baseball Analytics
Author: Peter Xenopoulos, Fernando Crema <www.peterxeno.com>
Description: A collection of baseball analytics functions for sabermetrics
purposes. Among these functions include popular metrics such as FIP, wOBA, and
runs created and other advanced metrics.
Packaged: 2016-01-26 06:35:20 UTC; pxenopoulos

Title: Generalized and Safe-Bayesian Ridge and Lasso Regression
Authors@R: c(person("Rianne", "de Heide", role = c("aut", "cre"),
person("Gustavo", "de los Campos", role = "ctb"),
person("Paulino", "Perez Rodriguez", role = "ctb"),
Description: Functions for Generalized and Safe-Bayesian Ridge and Lasso Regression models with both fixed and varying variance.
Maintainer: Rianne de Heide <r.de.heide@cwi.nl>

Title: A Toolbox for the Analysis of Proteomics Data
Description: Tools for the statistical analysis and visualization of (relative and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.
Imports: limma, gplots, seqinr, corrplot, optparse, data.table, epiR,

Description: Tools for the statistical analysis and visualization of (relative
and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.

Title: An Extension of the Taylor Diagram to Two-Dimensional Vector
Author: Jon Sáenz [aut, cph] (<https://orcid.org/0000-0002-5920-7570>),
Ganix Esnaola [ctb, cph] (<https://orcid.org/0000-0001-9058-043X>),
Maintainer: Santos J. González-Rojí <santosjose.gonzalez@ehu.eus>
Description: A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.