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simule

A Constrained L1 Minimization Approach for Estimating Multiple Sparse Gaussian or Nonparanormal Graphical Models

v1.3.0 · Jul 2, 2018 · GPL-2
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Description

This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.

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Check details (16 non-OK)
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Beilun Wang <bw4mw@virginia.edu>’

The BugReports field in DESCRIPTION has
  https://github.com/QData/SIMULE
which should likely be
  https://github.com/QData/SIMULE/issues
instead.
NOTE r-devel-linux-x86_64-debian-clang

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Beilun Wang <bw4mw@virginia.edu>’

The BugReports field in DESCRIPTION has
  https://github.com/QData/SIMULE
which should likely be
  https://github.com/QData/SIMULE/issues
instead.
NOTE r-devel-linux-x86_64-debian-gcc

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-linux-x86_64-fedora-clang

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-linux-x86_64-fedora-gcc

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-macos-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-patched-linux-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-linux-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-macos-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming th
...[truncated]...
ction. The \\eqn{\\epsilon} is
       |                              ^
checkRd: (-1) simule.Rd:69: Lost braces
    69 | matrices and it is the \\code{lambda} in our function. The \\eqn{\\epsilon} is
       |                                                                 ^
checkRd: (-1) simule.Rd:72: Lost braces
    72 | \\code{epsilon} parameter in our function and the default value is 1. For
       |       ^
checkRd: (-1) simule.Rd:47: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |       ^
checkRd: (-1) simule.Rd:47-48: Lost braces
    47 | \\item{Graphs}{A list of the estimated inverse
       |               ^
checkRd: (-1) simule.Rd:48: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^

Check History

NOTE 0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Beilun Wang <bw4mw@virginia.edu>’

The BugReports field in DESCRIPTION has
  https://github.com/QData/SIMULE
which should likely be
  https://github.com/QData/SIMULE/issues
instead.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Beilun Wang <bw4mw@virginia.edu>’

The BugReports field in DESCRIPTION has
  https://github.com/QData/SIMULE
which should likely be
  https://github.com/QData/SIMULE/issues
instead.
NOTE r-devel-linux-x86_64-fedora-clang

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-linux-x86_64-fedora-gcc

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-devel-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-patched-linux-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-linux-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-macos-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-release-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-macos-arm64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-macos-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^
NOTE r-oldrel-windows-x86_64

Rd files

checkRd: (-1) simule-package.Rd:18: Lost braces; missing escapes or markup?
    18 | Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task 
...[truncated]...
e share graph among
       |                                         ^
checkRd: (-1) simule.Rd:48-49: Lost braces
    48 | covariance/correlation matrices.} \\item{share}{The share graph among
       |                                                ^

Dependency Network

Dependencies Reverse dependencies lpSolve pcaPP igraph simule

Version History

new 1.3.0 Mar 10, 2026
updated 1.3.0 ← 1.2.0 diff Jul 1, 2018
updated 1.2.0 ← 1.1.2 diff Oct 31, 2017
updated 1.1.2 ← 1.1.1 diff Jul 30, 2017
updated 1.1.1 ← 1.1.0 diff Jul 11, 2017
updated 1.1.0 ← 1.0.0 diff May 24, 2017
new 1.0.0 Apr 18, 2017