Commit e214450f authored by Gilles Kratzer's avatar Gilles Kratzer
Browse files

error with httpS

parent d434f899
Pipeline #5263 passed with stage
in 4 seconds
......@@ -2,13 +2,13 @@ Package: mcmcabn
Title: Flexible Implementation of a Structural MCMC Sampler for DAGs
Version: 0.4
Authors@R: c(person("Gilles", "Kratzer", role = c("aut", "cre"),
email = "gilles.kratzer@math.uzh.ch",
email = "gilles.kratzer@gmail.com",
comment = c(ORCID = "0000-0002-5929-8935")),
person("Reinhard", "Furrer", role = c("ctb"),
email = "reinhard.furrer@math.uzh.ch",
comment = c(ORCID = "0000-0002-6319-2332")))
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>
Description: Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <doi:10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <http://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <https://dl.acm.org/doi/10.5555/1005332.1005352>, an uninformative prior and a user-defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting, and sampling the landscape of high scoring structures. It allows us to quantify the marginal impact of relationships of interest by marginalizing out over structures or nuisance dependencies. Structural MCMC seems an elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.
Maintainer: Gilles Kratzer <gilles.kratzer@gmail.com>
Description: Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <doi:10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <https://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <https://dl.acm.org/doi/10.5555/1005332.1005352>, an uninformative prior and a user-defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting, and sampling the landscape of high scoring structures. It allows us to quantify the marginal impact of relationships of interest by marginalizing out over structures or nuisance dependencies. Structural MCMC seems an elegant and natural way to estimate the true marginal impact, so one can determine if it's magnitude is big enough to consider as a worthwhile intervention.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment