Package: ebTobit 1.0.2
ebTobit: Empirical Bayesian Tobit Matrix Estimation
Estimation tools for multidimensional Gaussian means using empirical Bayesian g-modeling. Methods are able to handle fully observed data as well as left-, right-, and interval-censored observations (Tobit likelihood); descriptions of these methods can be found in Barbehenn and Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to accelerate many empirical Bayes and nonparametric maximum likelihood problems.
Authors:
ebTobit_1.0.2.tar.gz
ebTobit_1.0.2.zip(r-4.5)ebTobit_1.0.2.zip(r-4.4)ebTobit_1.0.2.zip(r-4.3)
ebTobit_1.0.2.tgz(r-4.5-x86_64)ebTobit_1.0.2.tgz(r-4.5-arm64)ebTobit_1.0.2.tgz(r-4.4-x86_64)ebTobit_1.0.2.tgz(r-4.4-arm64)ebTobit_1.0.2.tgz(r-4.3-x86_64)ebTobit_1.0.2.tgz(r-4.3-arm64)
ebTobit_1.0.2.tar.gz(r-4.5-noble)ebTobit_1.0.2.tar.gz(r-4.4-noble)
ebTobit_1.0.2.tgz(r-4.4-emscripten)ebTobit_1.0.2.tgz(r-4.3-emscripten)
ebTobit.pdf |ebTobit.html✨
ebTobit/json (API)
| # Install 'ebTobit' in R: |
| install.packages('ebTobit', repos = c('https://barbehenna.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/barbehenna/ebtobit/issues
- BileAcid - Bile Acid Data
Last updated 9 months agofrom:f5544ad261. Checks:1 OK, 10 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 31 2025 |
| R-4.5-win-x86_64 | NOTE | Jan 31 2025 |
| R-4.5-mac-x86_64 | NOTE | Jan 31 2025 |
| R-4.5-mac-aarch64 | NOTE | Jan 31 2025 |
| R-4.5-linux-x86_64 | NOTE | Jan 31 2025 |
| R-4.4-win-x86_64 | NOTE | Jan 31 2025 |
| R-4.4-mac-x86_64 | NOTE | Jan 31 2025 |
| R-4.4-mac-aarch64 | NOTE | Jan 31 2025 |
| R-4.3-win-x86_64 | NOTE | Jan 31 2025 |
| R-4.3-mac-x86_64 | NOTE | Jan 31 2025 |
| R-4.3-mac-aarch64 | NOTE | Jan 01 2025 |
Exports:ConvexDualConvexPrimalebTobitEMis.ebTobitlik_GaussianPIClikMatnew_ebTobitposterior_L1mediod.ebTobitposterior_mean.ebTobitposterior_mode.ebTobittobit_sdtobit_sd_mle
Dependencies:RcppRcppArmadilloRcppParallel
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bile Acid Data | BileAcid |
| Convex Optimization of the Kiefer-Wolfowitz NPMLE | ConvexDual |
| Convex Optimization of the Kiefer-Wolfowitz NPMLE | ConvexPrimal |
| Empirical Bayes Matrix Estimation under a Tobit Likelihood | ebTobit |
| Nonparametric Maximum Likelihood via Expectation Maximization | EM |
| Fitted Estimates of an ebTobit object | fitted.ebTobit |
| Validate ebTobit Object | is.ebTobit |
| Helper Function - generate likelihood for pair (L,R) and mean gr | lik_GaussianPIC |
| Helper Function - generate likelihood matrix | likMat |
| Marginal Log-likelihood of an ebTobit object | logLik.ebTobit |
| Create a new ebTobit object | new_ebTobit |
| Compute the Posterior L1 Mediod of an ebTobit object | posterior_L1mediod.ebTobit |
| Compute Posterior Mean of an ebTobit object | posterior_mean.ebTobit |
| Compute Posterior Mode of an ebTobit object | posterior_mode.ebTobit |
| Fitted Estimates of an ebTobit object | predict.ebTobit |
| Fit Tobit Standard Deviation via Maximum Likelihood | tobit_sd |
| Maximum Likelihood Estimator for a Single Standard Deviation Parameter | tobit_sd_mle |
