Introduction | Some theoretical results | Model with only fixed effects | Conditional means estimation under linear mixed models | Real data applications | Unconditional estimation | Log-normal regression | Random coefficient model | References
Package: BayesLN 0.2.12
BayesLN: Bayesian Inference for Log-Normal Data
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.
Authors:
BayesLN_0.2.12.tar.gz
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BayesLN_0.2.12.tgz(r-4.6-x86_64)BayesLN_0.2.12.tgz(r-4.6-arm64)BayesLN_0.2.12.tgz(r-4.5-x86_64)BayesLN_0.2.12.tgz(r-4.5-arm64)
BayesLN_0.2.12.tar.gz(r-4.7-arm64)BayesLN_0.2.12.tar.gz(r-4.7-x86_64)BayesLN_0.2.12.tar.gz(r-4.6-arm64)BayesLN_0.2.12.tar.gz(r-4.6-x86_64)
BayesLN_0.2.12.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BayesLN/json (API)
| # Install 'BayesLN' in R: |
| install.packages('BayesLN', repos = c('https://agardini.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- EPA09 - Chrysene concentration data
- fatigue - Low cycle fatigue data
- laminators - Laminators
- NCBC - Naval Construction Battalion Center data
- ReadingTime - Reading Times data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:19a0893361. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 158 | ||
| linux-devel-x86_64 | OK | 164 | ||
| source / vignettes | OK | 209 | ||
| linux-release-arm64 | OK | 155 | ||
| linux-release-x86_64 | OK | 167 | ||
| macos-release-arm64 | OK | 183 | ||
| macos-release-x86_64 | OK | 292 | ||
| macos-oldrel-arm64 | OK | 183 | ||
| macos-oldrel-x86_64 | OK | 343 | ||
| windows-devel | OK | 131 | ||
| windows-release | OK | 170 | ||
| windows-oldrel | OK | 133 | ||
| wasm-release | OK | 151 |
Exports:dlSMNGdSMNGGH_MGFLN_hier_existenceLN_hierarchicalLN_MeanLN_MeanRegLN_QuantLN_QuantRegmeanSMNGplSMNGpSMNGqlSMNGqSMNGrlSMNGrSMNGSMNG_MGFSMNGmoment
Dependencies:bootcodadata.tableDistributionUtilsGeneralizedHyperbolicgsllatticelme4MASSMatrixminqanlmenloptrnumDerivoptimxpracmarbibutilsRcppRcppEigenRdpackreformulasrlang
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Chrysene concentration data | EPA09 |
| Low cycle fatigue data | fatigue |
| GH Moment Generating Function | GH_MGF |
| Laminators | laminators |
| Numerical evaluation of the log-normal conditioned means posterior moments | LN_hier_existence |
| Bayesian estimation of a log - normal hierarchical model | LN_hierarchical |
| Bayesian Estimate of the Log-normal Mean | LN_Mean |
| Bayesian Estimate of the conditional Log-normal Mean | LN_MeanReg |
| Bayesian estimate of the log-normal quantiles | LN_Quant |
| Bayesian estimate of the log-normal conditioned quantiles | LN_QuantReg |
| Naval Construction Battalion Center data | NCBC |
| Reading Times data | ReadingTime |
| SMNG and logSMNG Distributions | dlSMNG dSMNG plSMNG pSMNG qlSMNG qSMNG rlSMNG rSMNG SMNGdistribution |
| SMNG Moments and Moment Generating Function | meanSMNG SMNGmoment SMNGmoments SMNG_MGF |
