Title: Explore the UK's National Power Grid Datasets
Description: The purpose of 'Relexon' is to allow R users to pull data from the UK's Balancing Mechanism Reporting Service, otherwise known as 'Elexon', using the 'BMRS' API <https://www.bmreports.com/bmrs/?q=help/about-us>. This can range from different types of electricity generation, how much electricity is used in a 5-minute period, imports, temperatures and forecasts. Users will require an 'Elexon' key, which are freely available by registering at <https://www.elexonportal.co.uk>.
Packaged: 2019-03-14 09:46:56 UTC; Phil.Hunter

Title: Functions for estimating parameters in software reliability models
Description: Functions for estimating parameters in software reliability models.
Only infinite failure models are implemented so far.
Packaged: Thu Nov 22 17:34:23 2007; Witti

Packaged: Sun Feb  1 14:29:13 2009; Witti

Title: Tools for structural reliability analysis
Description: A variety of tools useful for performing structural
reliability analysis, such as with structure function and
system signatures.  Plans to expand more widely.
Imports: sfsmisc, combinat, FRACTION, mcmc, PhaseType (>= 0.1.3),

Title: Tools for Structural Reliability Analysis

Title: Relative Importance PCA Regression
Author: Michael Hernandez <hernandez@uchicago.edu>, Yuri Balasanov
Description: Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013) <http://www-bcf.usc.edu/~gareth/ISL/>.
Imports: relaimpo, Rmisc, caret, ggplot2, reshape2