Commit 87ec6e34 authored by Gilles Kratzer's avatar Gilles Kratzer
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installation updated

parent 1c15f684
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^NEWS.md$
^README.md$
^mcmcabn_0.1.tar.gz$
^model.bug$
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## Quick start
You can install `mcmabn` with:
To install `mcmabn` you need essentially two R packages: `abn` and `gRbase` and those packages requires some libraries stored on bioconductor:
``` r
install.packages("mcmcabn")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RBGL","Rgraphviz", version = "3.8")
install.packages("mcmcabn", dependencies = TRUE)
```
The three main problems addressed by this R package are:
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(PUBLIC) !!! UNSTABLE VERSION !!!
mcmcabn is a one man show (me!) and made of more than 10000 lines of code which are not bug free! So use it with caution and awareness.
mcmcabn is a one man show (me!) and made of more than 10'000 lines of code which are not bug free! So use it with caution and awareness.
## Installation
install.packages("https://git.math.uzh.ch/gkratz/mcmcabn/raw/master/mcmcabn_0.1.tar.gz", repo=NULL, type="source")
Website: https://www.math.uzh.ch/pages/mcmcabn/
## 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) <http://dl.acm.org/citation.cfm?id=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 to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very 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.
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model{
###-----------------------
###Binomial nodes
###-----------------------
A ~ dbern(p.A); #Binary response
logit(p.A) <- 0 + 1*C; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
B ~ dbern(p.B); #Binary response
logit(p.B) <- 1 + 1*A + 1*E; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
C ~ dbern(p.C); #Binary response
logit(p.C) <- 0; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
D ~ dbern(p.D); #Binary response
logit(p.D) <- 0 + 1*E; #Logistic regression
###-----------------------
###Binomial nodes
###-----------------------
E ~ dbern(p.E); #Binary response
logit(p.E) <- 1 + 1*C; #Logistic regression
}
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