ILSM: Analyze Interconnection Structure of Multilayer Interaction Networks #661
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Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type |
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🚀 Editor check started 👋 |
Checks for ILSM (v1.0.3.2)git hash: 262b9217
Important: All failing checks above must be addressed prior to proceeding (Checks marked with 👀 may be optionally addressed.) Package License: MIT + file LICENSE 1. rOpenSci Statistical Standards (
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| type | package | ncalls |
|---|---|---|
| internal | base | 678 |
| internal | ILSM | 161 |
| imports | igraph | 62 |
| imports | Matrix | 13 |
| imports | stats | 3 |
| suggests | knitr | NA |
| suggests | rmarkdown | NA |
| suggests | testthat | NA |
| linking_to | NA | NA |
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.
base
c (102), matrix (70), sum (59), return (40), rownames (35), data.frame (34), rep (33), t (31), ncol (28), rowSums (28), length (26), colSums (24), for (16), list (15), as.matrix (14), apply (13), message (12), nrow (12), paste (12), unique (11), lapply (9), as.numeric (7), colnames (5), T (4), which (4), rbind (3), abs (2), drop (2), if (2), mean (2), round (2), sample (2), tcrossprod (2), unlist (2), eigen (1), F (1), Im (1), is.na (1), kronecker (1), max (1), min (1), mode (1), names (1), order (1), Re (1), replace (1), solve (1), sqrt (1), which.max (1)
ILSM
Two (42), Three (15), mEdges (10), Four (7), GetLargestEigenv_r (6), sumR_r (6), adject_net (5), GetMultiClosenessCentrality_r (5), GetMultiEigenvectorCentrality_r (5), GetMultiHubCentrality_r (5), GetMultiKatzCentrality_r (5), GetMultiPageRankCentrality_r (5), Kendall_cor (5), GetMultiAuthCentrality_r (4), emptyR (3), me_interlayer (3), vaznullR (3), BuildSupraAdjacencyMatrixFromExtendedEdgelist_r (2), BuildSupraTransitionMatrixFromSupraAdjacencyMatrix_r (2), edgelist_from_matrices (2), GetMultiPathStatistics_r (2), role_sim (2), SupraAdjacencyToBlockTensor_r (2), build_net (1), coid (1), cois (1), diagR_r (1), GetMultiRWCentrality_r (1), hc (1), icmotif_count (1), icmotif_role (1), igraph_from_matrices (1), Multi_motif (1), node_cv (1), null_model (1), pc (1), poc (1), SavueR (1)
igraph
V (49), graph_from_adjacency_matrix (8), distances (2), layout_with_sugiyama (2), get.edgelist (1)
Matrix
t (5), Matrix (3), sparseMatrix (3), Diagonal (2)
stats
C (1), lm (1), offset (1)
3. Statistical Properties
This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.
Details of statistical properties (click to open)
The package has:
- code in R (100% in 22 files) and
- 3 authors
- 1 vignette
- 1 internal data file
- 3 imported packages
- 13 exported functions (median 60 lines of code)
- 65 non-exported functions in R (median 19 lines of code)
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:
loc= "Lines of Code"fn= "function"exp/not_exp= exported / not exported
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function
The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.
| measure | value | percentile | noteworthy |
|---|---|---|---|
| files_R | 22 | 82.4 | |
| files_vignettes | 1 | 62.0 | |
| files_tests | 12 | 90.0 | |
| loc_R | 2281 | 84.5 | |
| loc_vignettes | 40 | 6.0 | |
| loc_tests | 441 | 68.9 | |
| num_vignettes | 1 | 59.0 | |
| data_size_total | 5642 | 66.4 | |
| data_size_median | 5642 | 74.6 | |
| n_fns_r | 78 | 68.2 | |
| n_fns_r_exported | 13 | 53.7 | |
| n_fns_r_not_exported | 65 | 72.4 | |
| n_fns_per_file_r | 2 | 38.5 | |
| num_params_per_fn | 2 | 8.2 | |
| loc_per_fn_r | 24 | 69.6 | |
| loc_per_fn_r_exp | 60 | 83.8 | |
| loc_per_fn_r_not_exp | 19 | 60.9 | |
| rel_whitespace_R | 8 | 66.6 | |
| rel_whitespace_vignettes | 22 | 5.4 | |
| rel_whitespace_tests | 12 | 57.1 | |
| doclines_per_fn_exp | 81 | 84.5 | |
| doclines_per_fn_not_exp | 0 | 0.0 | TRUE |
| fn_call_network_size | 115 | 80.3 |
3a. Network visualisation
Click to see the interactive network visualisation of calls between objects in package
4. goodpractice and other checks
Details of goodpractice checks (click to open)
3a. Continuous Integration Badges
GitHub Workflow Results
| id | name | conclusion | sha | run_number | date |
|---|---|---|---|---|---|
| 11073028649 | pkgcheck | failure | 262b92 | 9 | 2024-09-27 |
| 11073028622 | pkgdown.yaml | success | 262b92 | 18 | 2024-09-27 |
| 11073028626 | R-CMD-check.yaml | success | 262b92 | 17 | 2024-09-27 |
| 11073028621 | test-coverage.yaml | success | 262b92 | 17 | 2024-09-27 |
3b. goodpractice results
R CMD check with rcmdcheck
R CMD check generated the following check_fails:
- cyclocomp
- no_import_package_as_a_whole
Test coverage with covr
Package coverage: 82.45
Cyclocomplexity with cyclocomp
The following functions have cyclocomplexity >= 15:
| function | cyclocomplexity |
|---|---|
| Multi_motif | 99 |
| icmotif_role | 59 |
| cois | 25 |
| node_cv | 21 |
| Kendall_cor | 18 |
| coid | 17 |
| icmotif_count | 17 |
Static code analyses with lintr
lintr found the following 487 potential issues:
| message | number of times |
|---|---|
| Avoid 1:length(...) expressions, use seq_len. | 2 |
| Avoid 1:ncol(...) expressions, use seq_len. | 5 |
| Avoid 1:NCOL(...) expressions, use seq_len. | 2 |
| Avoid 1:nrow(...) expressions, use seq_len. | 22 |
| Avoid 1:NROW(...) expressions, use seq_len. | 2 |
| Avoid library() and require() calls in packages | 2 |
| Lines should not be more than 80 characters. This line is 100 characters. | 7 |
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| Use <-, not =, for assignment. | 10 |
5. Other Checks
Details of other checks (click to open)
✖️ The following 3 function names are duplicated in other packages:
-
hcfrom adana, bnlearn, mclust, multigraph
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null_modelfrom fabletools, insight, parsnip, qtlpoly
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pcfrom bReeze, FaultTree, gamlss.foreach, kader, lessR, pcalg, SEAsic, tabr, yasp
Package Versions
| package | version |
|---|---|
| pkgstats | 0.1.6.17 |
| pkgcheck | 0.1.2.58 |
| srr | 0.1.3.11 |
Editor-in-Chief Instructions:
Processing may not proceed until the items marked with ✖️ have been resolved.
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Hi @adamhsparks, I can't fix this check error: ✖️ Package name is not available (on CRAN). |
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yes, I'm aware, I checked your README and CRAN. Thanks for clarifying though! |
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Hi @WeichengSun, there's a few NOTEs that I've found when I run checks locally, could you please work to address them as I find a handling editor for {ILSM}? ❯ checking top-level files ... NOTE
Non-standard files/directories found at top level:
‘Contributing.Rmd’ ‘README.Rmd’ ‘codemeta.json’
❯ checking Rd files ... [0s/0s] NOTE
checkRd: (-3) coid.Rd:32: Lost braces
32 | \itemize{\item{\code{network.or.subnet_mat1}: input a 'igraph' of network data independently or input sparse matrix together with \code{subnet_mat2}.}}
| ^
checkRd: (-3) coid.Rd:35: Lost braces
35 | \itemize{\item{\code{network.or.subnet_mat1}: must input matrix(or data.frame) together with \code{subnet_mat2}. the matrix can be sparse matrix and matrix of interaction strength.}}
| ^
checkRd: (-3) coid.Rd:41: Lost braces
41 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) coid.Rd:42: Lost braces
42 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) coid.Rd:47: Lost braces
47 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) coid.Rd:49: Lost braces
49 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) coid.Rd:51: Lost braces
51 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) coid.Rd:52: Lost braces
52 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) coid.Rd:55: Lost braces
55 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) coid.Rd:57: Lost braces
57 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) coid.Rd:60: Lost braces
60 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) cois.Rd:32: Lost braces
32 | \itemize{\item{\code{network.or.subnet_mat1}: input a 'igraph' of network data independently or input sparse matrix together with \code{subnet_mat2}.}}
| ^
checkRd: (-3) cois.Rd:35: Lost braces
35 | \itemize{\item{\code{network.or.subnet_mat1}: must input matrix(or data.frame) together with \code{subnet_mat2}. the matrix can be sparse matrix and matrix of interaction strength.}}
| ^
checkRd: (-3) cois.Rd:41: Lost braces
41 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) cois.Rd:42: Lost braces
42 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) cois.Rd:47: Lost braces
47 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) cois.Rd:49: Lost braces
49 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) cois.Rd:51: Lost braces
51 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) cois.Rd:52: Lost braces
52 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) cois.Rd:55: Lost braces
55 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) cois.Rd:57: Lost braces
57 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) cois.Rd:60: Lost braces
60 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) hc.Rd:29: Lost braces
29 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) hc.Rd:30: Lost braces
30 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) hc.Rd:35: Lost braces
35 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) hc.Rd:36: Lost braces
36 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) hc.Rd:37: Lost braces
37 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) hc.Rd:38: Lost braces
38 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) icmotif_count.Rd:31: Lost braces
31 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) icmotif_count.Rd:32: Lost braces
32 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) icmotif_count.Rd:37: Lost braces
37 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) icmotif_count.Rd:39: Lost braces
39 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) icmotif_count.Rd:41: Lost braces
41 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) icmotif_count.Rd:42: Lost braces
42 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) icmotif_count.Rd:45: Lost braces
45 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) icmotif_count.Rd:47: Lost braces
47 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) icmotif_count.Rd:50: Lost braces
50 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) icmotif_role.Rd:31: Lost braces
31 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) icmotif_role.Rd:32: Lost braces
32 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) icmotif_role.Rd:37: Lost braces
37 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) icmotif_role.Rd:39: Lost braces
39 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) icmotif_role.Rd:41: Lost braces
41 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) icmotif_role.Rd:42: Lost braces
42 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) icmotif_role.Rd:45: Lost braces
45 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) icmotif_role.Rd:47: Lost braces
47 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) icmotif_role.Rd:50: Lost braces
50 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) igraph_from_matrices.Rd:31: Lost braces
31 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) igraph_from_matrices.Rd:33: Lost braces
33 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) igraph_from_matrices.Rd:35: Lost braces
35 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) igraph_from_matrices.Rd:36: Lost braces
36 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) igraph_from_matrices.Rd:39: Lost braces
39 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) igraph_from_matrices.Rd:41: Lost braces
41 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) igraph_from_matrices.Rd:44: Lost braces
44 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) node_cv.Rd:47: Lost braces
47 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) node_cv.Rd:48: Lost braces
48 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) node_cv.Rd:53: Lost braces
53 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) node_cv.Rd:55: Lost braces
55 | \item{When the two matrices can have different numbers of rows:}
| ^
checkRd: (-3) node_cv.Rd:57: Lost braces
57 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) node_cv.Rd:58: Lost braces
58 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) node_cv.Rd:61: Lost braces
61 | \item{When the two matrices can have the same numbers of rows:}
| ^
checkRd: (-3) node_cv.Rd:63: Lost braces
63 | \item{No matter how the row names of the two matrices are arranged, as long as the row names are exactly the same; But we don't handle matrices with empty row names (the function will give an error).}
| ^
checkRd: (-3) node_cv.Rd:66: Lost braces
66 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) node_cv.Rd:34-35: Lost braces
34 | \item{If \code{type} is either of "degree", "pagerank", "hub", "authority", "katz", "eigenvector", "closeness", the data frame has two columns, and the second column corresponds to either of "Degree", "Pagerank_versatility",
| ^
checkRd: (-3) node_cv.Rd:36-37: Lost braces
36 | \item{If \code{type} is "all", the data frame has eight columns, and columns form the second to the eighth correspond to "Degree", "Pagerank_versatility",
| ^
checkRd: (-3) null_model.Rd:30: Lost braces
30 | \item{For each of the four types of null models, there are corresponding algorithms. The first type, “subnetwork1”, involved scrambling the adjacency matrix of the first and second groups of the multilayer network.}
| ^
checkRd: (-3) null_model.Rd:31: Lost braces
31 | \item{The second type, “subnetwork2”, focused on scrambling the adjacency matrix of the second and third groups. }
| ^
checkRd: (-3) null_model.Rd:32: Lost braces
32 | \item{Comprehensively, the third type, “all”, blended the approaches of the first two to disarrange the entire network's adjacency matrix, achieving a thorough perturbation of the network's structure. }
| ^
checkRd: (-3) null_model.Rd:33: Lost braces
33 | \item{The last type named “Savue” that disarranged inherent structure in terms of the groups of species connected by each interconnecting species of every subnetworks, thus exhibiting different interconnection patterns.}}
| ^
checkRd: (-3) pc.Rd:29: Lost braces
29 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) pc.Rd:30: Lost braces
30 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) pc.Rd:35: Lost braces
35 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) pc.Rd:36: Lost braces
36 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) pc.Rd:37: Lost braces
37 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) pc.Rd:38: Lost braces
38 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
checkRd: (-3) poc.Rd:29: Lost braces
29 | \item{(1). Input in a network of type "igraph" alone.}
| ^
checkRd: (-3) poc.Rd:30: Lost braces
30 | \item{(2). Must be entered as data frame or matrix with \code{subnet_mat2}.}
| ^
checkRd: (-3) poc.Rd:35: Lost braces
35 | \item{Try to make the rows of both matrices have the same attributes. Or we default:}
| ^
checkRd: (-3) poc.Rd:36: Lost braces
36 | \item{(1). If both matrices have row names, then the function counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) poc.Rd:37: Lost braces
37 | \item{(2). If at most one matrix has row names, the function assigns new row names to both matrices on a row-to-row basis (any extra row names are assigned a new value) and then counts all row names to produce two new matrices with the same row names.}
| ^
checkRd: (-3) poc.Rd:38: Lost braces
38 | \item{The two matrices can have different numbers of rows, but read our default handling carefully to make sure the calculation is accurate when using this function!!!}
| ^
0 errors ✔ | 0 warnings ✔ | 2 notes ✖ |
|
Hi @adamhsparks, thank you, I have addressed these NOTEs, please continue to promote this issue. |
|
Thanks, @WeichengSun,
✖ checking Rd files ... [0s/0s] NOTE checkRd: (-3)
node_cv.Rd:34-35: Lost braces 34 | \item{If \code{type} is either
of "degree", "pagerank", "hub", "authority", "katz", "eigenvector",
"closeness", the data frame has two columns, and the second column
corresponds to either of "Degree", "Pagerank_versatility", | ^
checkRd: (-3) node_cv.Rd:36-37: Lost braces 36 | \item{If
\code{type} is "all", the data frame has eight columns, and columns
form the second to the eighth correspond to "Degree",
"Pagerank_versatility", | ^
|
|
OK @adamhsparks, I will continue to complete the issue, and I also accept your advice to put it on hold. Since this is only a tool for our scientific research, but our research work still needs to continue. Please inform me and provide other relevant requirements after you finish the working on a category for network standards. Thank you. |
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Submitting Author Name: Weicheng Sun
Submitting Author Github Handle: @WeichengSun
Other Package Authors Github handles: (comma separated, delete if none)
Repository: https://github.com/WeichengSun/ILSM
Version submitted: 1.0.3.2
Submission type: Standard
Editor: TBD
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
This package that wrap professional programs used for multilayer interaction network research. These programs must be specific to interconnection structure. It focuses on interacnnection motif for handling matrices or 'igraph'data.
In community ecology, ecological network is used to represent the complex ecological interaction system. Therefore, researchers in this field often pay attention to the work from the network perspective. Therefore, this package is based on the multilayer ecological network to study the interconnection structure, hoping to provide convenience for the mentioned audience.
There is an R package(bmotif) that does similar work in the field of ecological networks, but the program provided by it is only used to study the structure of bipartite networks and is relatively simple, while the new package only studies the structure of multilayer interacting networks, which is more complex.
n/a
#654
pkgcheckitems which your package is unable to pass.Because we have submitted the package ILSM to CRAN ahead of time, the package name is duplicated and the
pkgcheckitems result is not available.Technical checks
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