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weibulltools

Unlike other R packages for survival analysis, the weibulltools package focuses on reliability methods and has the advantage of being open source and easily accessible contrary to other reliability analysis software. In addition to that the package can be integrated into (partly) automated data analysis processes and even can be connected to big data systems.

The weibulltools package 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 yet, taking account of registering and reporting delays. On this basis, if the products looked upon are vehicles, the covered mileage can be estimated as well. The weibulltools package also provides methods for probability estimation within samples that contain failures as well as censored data. Methods for estimating the parameters of lifetime distributions as well as the confidence intervals of quantiles and probabilities 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, methods for interactive visualization of the edited data using Plotly are provided as well.

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Version

Install

install.packages('weibulltools')

Monthly Downloads

260

Version

1.0.1

License

GPL-2

Maintainer

Hensel Tim-Gunnar

Last Published

January 29th, 2019

Functions in weibulltools (1.0.1)

calculate_ranks

Computation of Johnson Ranks
dist_delay_register

Parameter Estimation of the Delay in Registration Distribution
confint_betabinom

Beta Binomial Confidence Bounds for Quantiles and/or Probabilities
delta_method

Delta Method for Parametric Lifetime Distributions
confint_fisher

Fisher Confidence Bounds for Quantiles and/or Probabilities
loglik_profiling

Log-Likelihood Profile Function for Log-Location-Scale Distributions with Threshold
mcs_delay_report

Adjustment of Operating Times by Delays in Report using a Monte Carlo Approach
dist_mileage

Parameter Estimation of the Mileage Distribution
mixmod_regression

Mixture Model Identification using Segmented Regression
johnson_method

Estimation of Failure Probabilities using Johnson's Method
mcs_delays

Adjustment of Operating Times by Delays using a Monte Carlo Approach
mcs_delay_register

Adjustment of Operating Times by Delays in Registration using a Monte Carlo Approach
dist_delay_report

Parameter Estimation of the Delay in Report Distribution
mcs_mileage

Estimation of Driving Distances for Censored Observations using a Monte Carlo Approach
mixture_em_cpp

EM-Algorithm using Newton-Raphson Method
plot_mod_mix

Adding Estimated Population Lines of a Separated Mixture Model to a Probability Plot
predict_prob

Estimation of Failure Probabilities for Parametric Lifetime Distributions
loglik_function

Log-Likelihood Function for (Log-) Location-Scale Distributions (with Threshold)
predict_quantile

Estimation of Quantiles for Parametric Lifetime Distributions
plot_prob

Probability Plotting Method for Univariate Lifetime Distributions
plot_pop

Add Population Line to an Existing Grid
ml_estimation

ML Estimation for Parametric Lifetime Distributions
nelson_method

Estimation of Failure Probabilities using the Nelson-Aalen Estimator
mixmod_em

Mixture Model Estimation using EM-Algorithm
weibulltools

weibulltools
plot_conf

Add Confidence Region(s) for Quantiles or Probabilities
mr_method

Estimation of Failure Probabilities using Median Ranks
plot_prob_mix

Probability Plot for Separated Mixture Models
r_squared_profiling

\(R<U+00B2>\)-Profile Function for Log-Location-Scale Distributions with Threshold
rank_regression

Rank Regression for Parametric Lifetime Distributions
plot_layout

Layout of the Probability Plot
plot_mod

Adding an Estimated Population Line to a Probability Plot
kaplan_method

Estimation of Failure Probabilities using Kaplan-Meier