This package is considered a duplicate. The official version of this package is found at:https://audreyfulab.r-universe.dev/MRPC

Package: MRPC 3.2.0

Audrey Fu

MRPC: PC Algorithm with the Principle of Mendelian Randomization

A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>, Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>, Kvamme and Badsha, et al. (2025) <doi:10.1093/genetics/iyaf064>.

Authors:Md Bahadur Badsha [aut], Evan A Martin [ctb], Audrey Fu [aut, cre]

MRPC_3.2.0.tar.gz
MRPC_3.2.0.zip(r-4.7)MRPC_3.2.0.zip(r-4.6)MRPC_3.2.0.zip(r-4.5)
MRPC_3.2.0.tgz(r-4.6-any)MRPC_3.2.0.tgz(r-4.5-any)
MRPC_3.2.0.tar.gz(r-4.7-any)MRPC_3.2.0.tar.gz(r-4.6-any)
MRPC_3.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MRPC/json (API)

# Install 'MRPC' in R:

install.packages('MRPC', repos = c('https://mrcieu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/audreyqyfu/mrpc/issues

Datasets:

data_examples - Example data under simple and complex models
data_GEUVADIS - GEUVADIS data with 62 eQTL-gene sets
data_GEUVADIS_combined - Combined genotype and gene expression data from 62 eQTL-gene sets in 373 Europeans from GEUVADIS
data_with_outliers - Example data with outliers
data_without_outliers - Example data without outliers
MRPCtruth - Graphs used as truth in simulation
simu_data_layered - Data for the layered model
simu_data_M0 - Data for Model 0
simu_data_M1 - Data for Model 1
simu_data_M2 - Data for Model 2
simu_data_M3 - Data for Model 3
simu_data_M4 - Data for Model 4
simu_data_multiparent - Data for the multiple-parent model
simu_data_starshaped - Data for the star model

On CRAN:

4.10 score 8 stars 26 scripts 679 downloads 2 mentions 25 exports 146 dependencies

Last updated from:c4968a4c67. Checks:9 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	260
source / vignettes	OK	295
linux-release-x86_64	OK	292
macos-release-arm64	OK	117
macos-oldrel-arm64	OK	186
windows-devel	OK	175
windows-release	OK	164
windows-oldrel	OK	188
wasm-release	OK	239

Exports:AdjustMatrix aSHD CompareMethodsNodeOrdering CompareMethodsVStructure CutModules EdgeOrientation empty IdentifyAssociatedPCs ModiSkeleton mpinv MRPC plot PlotDendrogram PlotGraphWithModules print RecallPrecision RobustCor seqDiff SeqFDR SimulateData SimulateData1P SimulateData2P SimulateData3P SimulateDataNP summary

Dependencies:abind backports base64enc bayesm bdsmatrix BH BiocGenerics BiocManager bit bit64 bnlearn boot broom bslib cachem checkmate cli clipr clue cluster coda codetools colorspace compositions corpcor cpp11 crayon data.table DEoptimR digest doParallel dplyr dynamicTreeCut evaluate farver fastcluster fastICA fastmap fontawesome forcats foreach foreign Formula fs generics GGally ggm ggplot2 ggstats glmnet glue GPArotation graph gridExtra gtable gtools haven highr Hmisc hms htmlTable htmltools htmlwidgets igraph impute isoband iterators jomo jquerylib jsonlite knitr labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix matrixStats memoise mice mime minqa mitml mnormt network nlme nloptr nnet numDeriv ordinal pan patchwork pcalg pillar pkgconfig plyr preprocessCore prettyunits progress psych purrr R6 rappdirs RBGL rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr reformulas Rgraphviz rlang rmarkdown robustbase rpart rstudioapi S7 sass scales sfsmisc shape statnet.common stringi stringr survival tensorA tibble tidyr tidyselect tinytex tzdb ucminf utf8 vcd vctrs viridisLite vroom WGCNA withr xfun yaml zoo

Help page	Topics
Adjust the columns of the input matrix same as in the reference matrix	AdjustMatrix
Adjusted structural hamming distance (aSHD)	aSHD
Comparison of inference accuracy using the same data but with different node orderings.	CompareMethodsNodeOrdering
Comparison of inference accuracy of different methods on data with and without a v-structure	CompareMethodsVStructure
Cut a numeric variable into intervals	CutModules
Example data under simple and complex models	data_examples
GEUVADIS data with 62 eQTL-gene sets	data_GEUVADIS
Combined genotype and gene expression data from 62 eQTL-gene sets in 373 Europeans from GEUVADIS	data_GEUVADIS_combined
Example data with outliers	data_with_outliers
Example data without outliers	data_without_outliers
Perform edge orientation under the MRPC algorithm	EdgeOrientation
Check empty matrix	empty
Identifyprincipal components (PCs) that are significantly associated with eQTLs and genes	IdentifyAssociatedPCs
Infer a graph skeleton (undirected graph)	ModiSkeleton
Calculate the inverse matrix	mpinv
Infer a causal network using the MRPC algorithm	MRPC
Class of MRPC algorithm results	MRPCclass-class plot,MRPCclass,ANY-method print,MRPCclass-method show,MRPCclass-method summary,MRPCclass-method
Graphs used as truth in simulation	MRPCtruth
Plot a dendrogram and display node groups in colored modules	PlotDendrogram
Plot a graph with nodes in modules indicated by colors	PlotGraphWithModules
Calculate recall and precision for two graphs	RecallPrecision
Calculate robust correlation matrix	RobustCor
Deviation between two graphs represented by two sequences	seqDiff
Sequential FDR	SeqFDR
Data for the layered model	simu_data_layered
Data for Model 0	simu_data_M0
Data for Model 1	simu_data_M1
Data for Model 2	simu_data_M2
Data for Model 3	simu_data_M3
Data for Model 4	simu_data_M4
Data for the multiple-parent model	simu_data_multiparent
Data for the star model	simu_data_starshaped
Simulate data under certain graphs	SimulateData
Simulate data for a node with one parent	SimulateData1P
Simulate data for a node with two parents	SimulateData2P
Simulate data for a node with three parents	SimulateData3P
Simulate data for a node with no parent	SimulateDataNP

Package: MRPC 3.2.0

MRPC: PC Algorithm with the Principle of Mendelian Randomization

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