Introduction | Complete (Uncensored) Data | Right-Censored Data | Left-Censored Data | Interval-Censored Data | Type-I Censoring | Type-II Censoring | Progressive Type-II Censoring | Hybrid Censoring | Doubly Censored Data | Comparing Censoring Schemes | Tips for Working with Censored Data | Next Steps
Package: TKApprox 0.1.0
TKApprox: A General Framework for Bayesian Estimation Using the 'Tierney'-'Kadane' Approximation
Provides a distribution-independent framework for Bayesian estimation of arbitrary univariate probability models using the 'Tierney'-'Kadane' approximation ('Tierney' & 'Kadane', 1986 <doi:10.1080/01621459.1986.10478240>). Users specify the probability distribution, likelihood, prior distributions, and censoring mechanism, while the package automatically constructs the posterior distribution, computes posterior modes and Hessian matrices, approximates posterior expectations under several Bayesian loss functions, and returns Bayesian parameter estimates, posterior covariance matrices, credible intervals, diagnostic plots, and model comparison statistics. Supports complete, right-, left-, interval-, Type-I, Type-II, progressive Type-II, hybrid, and doubly censored data ('Lawless', 2003 <ISBN:978-0-471-37215-8>; 'Meeker' & 'Escobar', 1998 <ISBN:978-0-471-14328-4>; 'Balakrishnan' & 'Aggarwala', 2000 <ISBN:978-0-8176-4129-0>; 'Kundu' & 'Pradhan', 2009 <doi:10.1198/TECH.2009.0019>), making it a flexible tool for Bayesian reliability, survival, and lifetime data analysis.
Authors:
TKApprox_0.1.0.tar.gz
TKApprox_0.1.0.zip(r-4.7-any)TKApprox_0.1.0.zip(r-4.6-any)TKApprox_0.1.0.zip(r-4.5-any)
TKApprox_0.1.0.tgz(r-4.6-any)TKApprox_0.1.0.tgz(r-4.5-any)
TKApprox_0.1.0.tar.gz(r-4.7-any)TKApprox_0.1.0.tar.gz(r-4.6-any)
TKApprox_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TKApprox/json (API)
| # Install 'TKApprox' in R: |
| install.packages('TKApprox', repos = c('https://shikhartyagi.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:238c54ce6f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 140 | ||
| source / vignettes | OK | 246 | ||
| linux-release | OK | 145 | ||
| macos-release | OK | 93 | ||
| macos-oldrel | OK | 103 | ||
| windows-devel | OK | 90 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 64 | ||
| wasm-release | OK | 191 |
Exports:CAICDICeval_logposteriorexpected_logposteriorHQICloglik_completeloglik_dispatcherloglik_doubly_censoredloglik_hybrid_censoredloglik_interval_censoredloglik_left_censoredloglik_progressive_type2_censoredloglik_right_censoredloglik_type1_censoredloglik_type2_censoredlogprior_betalogprior_dispatcherlogprior_exponentiallogprior_gammalogprior_independentlogprior_invgammalogprior_lognormallogprior_normallogprior_uniformlogprior_weibullmodel_comparison_tableneglogLikplot_convergenceplot_credible_intervalsplot_likelihood_surfaceplot_posterior_approximationplot_prior_posteriorplot_residualsplot_sensitivity_estimatesplot_sensitivity_logposteriorplot_sensitivity_riskprint_model_comparisontk_expectationtk_expectationstk_fittk_hessiantk_losstk_loss_customtk_loss_geltk_loss_linextk_loss_precautionarytk_loss_seltk_loss_weighted_seltk_modetk_plottk_posteriortk_sensitivity
Dependencies:backportscheckmatedigestgenericslatticeMASSmaxLikmiscToolsnumDerivrlangsandwichzoo
Censoring Schemes
Last update: 2026-08-07
Started: 2026-08-07
Defining Custom Distributions
Introduction | Required Functions | Continuous Distributions | Example 1: Log-Normal Distribution | Example 2: Pareto Distribution | Example 3: Burr Type XII Distribution | Discrete Distributions | Example 4: Poisson Distribution | Example 5: Negative Binomial Distribution | Using Custom Log-Likelihood Functions | Tips for Defining Distributions | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Introduction to TKApprox
Introduction | Basic Workflow | A Simple Example: Exponential Distribution | Step 1: Define the Distribution | Step 2: Specify Prior | Step 3: Generate Data | Step 4: Fit the Model | Step 5: Examine Results | Visualization | Different Loss Functions | Squared Error Loss (Posterior Mean) | LINEX Loss | General Entropy Loss | Censored Data | Prior Sensitivity Analysis | Multi-Parameter Models | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Loss Functions
Introduction | Squared Error Loss (SEL) | LINEX Loss | LINEX Sensitivity Analysis | General Entropy Loss (GEL) | Precautionary Loss | Weighted Squared Error Loss | Custom Loss Functions | Comparing All Loss Functions | Choosing a Loss Function | Multi-Parameter Models | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Prior Specification
Introduction | Standard Prior Families | Gamma Prior | Normal Prior | Beta Prior | Uniform Prior | Exponential Prior | Log-Normal Prior | Weibull Prior | Inverse Gamma Prior | Independent Priors for Multiple Parameters | Custom Prior Functions | Non-Informative (Flat) Priors | Prior Sensitivity Analysis | Systematic Prior Sensitivity | Choosing Prior Hyperparameters | Conjugate Priors | Weakly Informative Priors | Informative Priors | Tips for Prior Specification | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Real Data Example
Introduction | Dataset: Air Conditioning System Failure Times | Exploratory Data Analysis | Weibull Distribution Model | Define the Distribution | Specify Priors | Fit the Model with Complete Data | Examine Results | Model Comparison Statistics | Visualization | Analysis with Different Loss Functions | LINEX Loss | General Entropy Loss | Comparison of Loss Functions | Analysis with Censored Data | Create Right-Censored Data | Fit with Right Censoring | Compare Complete vs Censored | Reliability Function Estimation | Hazard Function Estimation | Prior Sensitivity Analysis | Prediction | Model Diagnostics | Comparison with Maximum Likelihood Estimation | Summary and Interpretation | Key Findings | Practical Implications | References | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Simulation Studies
Introduction | Basic Simulation Framework | Example 1: Exponential Distribution with Complete Data | Setup | Simulation Function | Run Simulations | Compute Performance Metrics | Display Results | Visualize Results | Example 2: Censoring Schemes Comparison | Simulation Function for Right Censoring | Compute Metrics | Visualize | Example 3: Prior Sensitivity Simulation | Example 4: Coverage Probability | Run Simulation | Example 5: Multi-Parameter Model Simulation | Parallel Simulation | Tips for Simulation Studies | Interpreting Simulation Results | Bias | Variance | MSE | Coverage Probability | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
Tierney-Kadane Approximation: Theory and Derivation
Introduction | Problem Statement | Laplace Approximation Background | Tierney-Kadane Derivation | Step 1: Posterior Mode | Step 2: Modified Function for Expectation | Step 3: TK Approximation Formula | Properties of the TK Approximation | Accuracy | Requirements | Advantages | Limitations | Implementation in TKApprox | Function Structure | Example: Computing Posterior Mean | Connection to Loss Functions | Squared Error Loss (Posterior Mean) | LINEX Loss | General Entropy Loss | Precautionary Loss | Weighted Squared Error Loss | Numerical Considerations | Optimization | Numerical Differentiation | Numerical Stability | Comparison with Other Methods | vs. MCMC | vs. Variational Inference | vs. Importance Sampling | References | Next Steps
Last update: 2026-08-07
Started: 2026-08-07
