Package: weibulltools
Type: Package
Title: Statistical Methods for Life Data Analysis
Version: 1.0.1
Authors@R: person("Hensel", "Tim-Gunnar", 
           email = "tim-gunnar.hensel@tu-berlin.de", 
           role = c("aut", "cre")) 
Description: Contains methods for examining bench test or field data using the 
             well-known Weibull Analysis. It includes Monte Carlo simulation for 
             estimating the life span of products that have not failed, taking 
             account of registering and reporting delays as stated in 
             (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). 
             If the products looked upon are vehicles, the covered mileage can 
             be estimated as well. 
             It also provides 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.   
             Methods for estimating the parameters of lifetime distributions, like 
             Maximum Likelihood and Median-Rank Regression, 
             (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) 
             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 are also included. 
             If desired, the data can automatically be divided into subgroups 
             using segmented regression. And if the number of subgroups in a 
             Weibull Mixture Model is known, data can be analyzed using the 
             EM-Algorithm. 
             Besides the calculation, methods for interactive visualization of 
             the edited data using *plotly* are provided as well. These 
             visualizations include the layout of a probability plot for a 
             specified distribution, the graphical technique of probability plotting 
             and the possibility of adding regression lines and confidence bounds 
             to existing plots. 
License: GPL-2
Imports: dplyr, LearnBayes, magrittr, plotly, Rcpp, sandwich,
        segmented, SPREDA, survival
LinkingTo: Rcpp (>= 0.12.18), RcppArmadillo
Depends: R (>= 3.3.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: ggplot2, knitr, rmarkdown, tidyverse
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-01-29 15:04:07 UTC; Tim.Hensel
Author: Hensel Tim-Gunnar [aut, cre]
Maintainer: Hensel Tim-Gunnar <tim-gunnar.hensel@tu-berlin.de>
Repository: CRAN
Date/Publication: 2019-01-29 16:10:03 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2020-08-05 07:59:02 UTC; windows
Archs: i386, x64
