Package index • r2IGP

All functions

Approx.integration(): Approximate Integration Method Selector

CI_Bootstrap(): Bootstrap Confidence Interval for Model Parameters

EM(): EM Algorithm: Expectation-Maximization (EM) Algorithm

EM_iter_plot(): Plotting EM Algorithm Iteration Results

E_z(): EM Algorithm: Expectation Step (E-step)

Integrand_fun(): Integrand Function for the Gaussian-Legendre Integral

Lambda_cum(): Cumulative Lambda Calculation

Lambda_fun(): Lambda Function Calculation

Lambda_fun_der(): Lambda Function Derivative Calculation

Log.liklihod(): Compute Log-Likelihood for Various Models

M4.loglik(): Compute Log-Likelihood for the M4 Model

Reliability(): Compute Reliability over Time

degradation.path.plot.summary(): Degradation Path Plot Summary This function generates the degradation path plot based on different lambda functions for time and cycles.

drIG(): Probability Density Function of the Reparameterized Inverse Gaussian (drIG)

f_uz_given_D_gaussian(): Estimate Function for f(u_ij | D)

fit.path.plot(): Plot Fitted Path with or without Confidence Intervals

fit.path.process(): Compute Fitted Path for Each Product

gaussian_legendre_integral(): Gaussian-Legendre Integration

init.guess(): Initial Parameter Guess Using Optimization

init.log.likelihood(): Initial Log-Likelihood Calculation

mc_integral(): Monte Carlo Integration

move_infinite(): Function to Remove Rows Containing Infinite Values or Exceeding Thresholds

path.3D.plot(): 3D Path Plot of Degradation Loss

performence.compare(): Function to Compute Performance Metrics (RB, MSE, RRMSE, CP, LEN)

prIG(): Cumulative Distribution Function of the Reparameterized Inverse Gaussian (prIG)

rrIG(): Reparameterized Inverse Gaussian Random Number Generator (rIG)

save.result(): Function to Save Simulation Results

selected_fun(): Function to Filter Rows Based on Criteria

sim.dat.path(): Simulate Degradation Data

trapezoidal_integral(): Trapezoidal Rule Integration

uncon_lifetime_CDF(): Reliability Function Calculation