weibulltools: Statistical Methods for Life Data Analysis

Provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, <ISBN:978-0-9653062-3-2>), Johnson (Johnson, 1964, <ISBN:978-0444403223>), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, <ISBN:9780471673279>) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported.

Package details

AuthorTim-Gunnar Hensel [aut, cre], David Barkemeyer [aut]
MaintainerTim-Gunnar Hensel <tim-gunnar.hensel@tu-berlin.de>
LicenseGPL-2
Version2.1.0
URL https://tim-tu.github.io/weibulltools/ https://github.com/Tim-TU/weibulltools
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("weibulltools")

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weibulltools documentation built on April 5, 2023, 5:10 p.m.