BayesSummaryStatLM
2.0MCMC Sampling of Bayesian Linear Models via Summary Statistics
Overview
Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions: one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks. See Miroshnikov, Savel'ev and Conlon (2015) arXiv:1503.00635.
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- Return-value docs
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- References docs
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People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-09-28issues were not corrected in time
- RR 4.2.0 released · 2022-04-22
- 2.02021-07-01 · diff ↗
- unarchivedReturned to CRAN2021-07-01
- RR 4.1.0 released · 2021-05-18
- archivedRemoved from CRAN2020-08-14check problems were not corrected in time
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- 1.0-12015-01-01 · diff ↗
- 1.02015-01-01
- RR 3.1.0 released · 2014-04-10
Package metadata
- Total releases
- 3
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.1.1
- Bundled data
- 1.1 MB / 4 files
- Download size
- not tracked yet
- Installed size
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- With dependencies
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