software-review ILSM: Analyze Interconnection Structure of Multilayer Interaction Networks

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

Paste the full DESCRIPTION file inside a code block below:

Package: ILSM
Title: Analyze Interconnection Structure of Multilayer Interaction Networks
Version: 1.0.3.2
Authors@R: c(
    person("Weicheng", "Sun", , "[email protected]", role = c("aut", "cre")),
    person("Chuan", "Yan", , "[email protected]", role = c("aut")),
    person("Yangyang", "Zhao", , "[email protected]", role = c("aut")))
Description: 
    In view of the analysis of the structural characteristics of the multilayer 
    network has been complete, however, there is still a lack of a unified 
    operation that can quickly obtain the corresponding characteristics of the 
    multilayer network. 
    To solve this insufficiency, 'ILSM' was designed for supporting calculating 
    such metrics of multilayer networks by functions of this R package.
License: MIT + file LICENSE
URL: https://github.com/WeichengSun/ILSM
BugReports: https://github.com/WeichengSun/ILSM/issues
Encoding: UTF-8
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.1
Depends: R (>= 3.5.0)
Imports: 
    stats,
    Matrix,
    igraph
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3

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.):
- [ ] data retrieval
- [ ] data extraction
- [x] data munging
- [ ] data deposition
- [ ] data validation and testing
- [ ] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [x] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
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.

Who is the target audience and what are scientific applications of this package?

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.

Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

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.

(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

n/a

If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

#654

Explain reasons for any pkgcheck items 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 pkgcheck items result is not available.

Technical checks

Confirm each of the following by checking the box.

[x] I have read the rOpenSci packaging guide.
[x] I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has a CRAN and OSI accepted license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions, created with roxygen2.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, including reporting of test coverage.

Publication options

[ ] Do you intend for this package to go on CRAN?
[ ] Do you intend for this package to go on Bioconductor?
[x] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

[x] The package is novel and will be of interest to the broad readership of the journal.
[x] The manuscript describing the package is no longer than 3000 words.
[ ] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see MEE's Policy on Publishing Code)
(Scope: Do consider MEE's Aims and Scope for your manuscript. We make no guarantee that your manuscript will be within MEE scope.)
(Although not required, we strongly recommend having a full manuscript prepared when you submit here.)
(Please do not submit your package separately to Methods in Ecology and Evolution)

Code of conduct

[x] I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

Sep 28 '24 14:09 WeichengSun

Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type @ropensci-review-bot help for help.

Sep 28 '24 14:09 ropensci-review-bot

:rocket:

Editor check started

:wave:

Sep 28 '24 14:09 ropensci-review-bot

Checks for ILSM (v1.0.3.2)

git hash: 262b9217

:heavy_multiplication_x: Package name is not available (on CRAN).
:heavy_check_mark: has a 'codemeta.json' file.
:heavy_check_mark: has a 'contributing' file.
:heavy_check_mark: uses 'roxygen2'.
:heavy_check_mark: 'DESCRIPTION' has a URL field.
:heavy_check_mark: 'DESCRIPTION' has a BugReports field.
:heavy_check_mark: Package has at least one HTML vignette
:heavy_check_mark: All functions have examples.
:heavy_check_mark: Package has continuous integration checks.
:heavy_check_mark: Package coverage is 82.5%.
:heavy_check_mark: R CMD check found no errors.
:heavy_check_mark: R CMD check found no warnings.
:eyes: Function names are duplicated in other packages

Important: All failing checks above must be addressed prior to proceeding

(Checks marked with :eyes: may be optionally addressed.)

Package License: MIT + file LICENSE

1. rOpenSci Statistical Standards (`srr` package)

:heavy_check_mark: All applicable standards [v0.2.0] have been documented in this package (46 complied with; 27 N/A standards)

2. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

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
Lines should not be more than 80 characters. This line is 102 characters.	12
Lines should not be more than 80 characters. This line is 103 characters.	2
Lines should not be more than 80 characters. This line is 104 characters.	10
Lines should not be more than 80 characters. This line is 105 characters.	4
Lines should not be more than 80 characters. This line is 106 characters.	1
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Lines should not be more than 80 characters. This line is 162 characters.	1
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Lines should not be more than 80 characters. This line is 168 characters.	3
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Lines should not be more than 80 characters. This line is 174 characters.	1
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Lines should not be more than 80 characters. This line is 255 characters.	9
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Lines should not be more than 80 characters. This line is 257 characters.	1
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Lines should not be more than 80 characters. This line is 88 characters.	19
Lines should not be more than 80 characters. This line is 89 characters.	6
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Lines should not be more than 80 characters. This line is 91 characters.	4
Lines should not be more than 80 characters. This line is 92 characters.	13
Lines should not be more than 80 characters. This line is 93 characters.	7
Lines should not be more than 80 characters. This line is 94 characters.	9
Lines should not be more than 80 characters. This line is 95 characters.	27
Lines should not be more than 80 characters. This line is 96 characters.	7
Lines should not be more than 80 characters. This line is 97 characters.	14
Lines should not be more than 80 characters. This line is 98 characters.	1
Lines should not be more than 80 characters. This line is 99 characters.	4
Use <-, not =, for assignment.	10

5. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 3 function names are duplicated in other packages:

- hc from adana, bnlearn, mclust, multigraph
- null_model from fabletools, insight, parsnip, qtlpoly
- pc from 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 :heavy_multiplication_x: have been resolved.

Sep 28 '24 14:09 ropensci-review-bot

Hi @adamhsparks, I can't fix this check error: ✖️ Package name is not available (on CRAN). Because the ILSM on CRAN and this submission here are all my projects

Sep 29 '24 08:09 WeichengSun

yes, I'm aware, I checked your README and CRAN. Thanks for clarifying though!

Sep 29 '24 08:09 adamhsparks

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 ✖

Sep 29 '24 09:09 adamhsparks

Hi @adamhsparks, thank you, I have addressed these NOTEs, please continue to promote this issue.

Oct 09 '24 03:10 WeichengSun

Thanks, @WeichengSun,

I still see an issue with missing braces as shown here:

  ✖ 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", | ^

Some of the other editors have looked over the repository and feel that it doesn't fit the categories you've elected, "Software Wrapper" or "Data Munging" But it does fit a category that we're actively preparing. Can I ask you to hold off on this for a bit? We're still working on a category for network standards where we think that this would fit, https://github.com/ropensci/statistical-software-review-book/pull/67. Once that's activated, we'll have a home for this but it will require you to document adherence to the network standards.

Oct 09 '24 05:10 adamhsparks

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. Mutual encouragement.

Oct 09 '24 13:10 WeichengSun

@WeichengSun FYI my EiC rotation just started. I see we're holding until https://github.com/ropensci/statistical-software-review-book/pull/67 is merged. I just asked for it's status at https://github.com/ropensci/statistical-software-review-book/pull/67#issuecomment-2629422637. Looking forward to continuing soon.

Feb 02 '25 14:02 maurolepore

@WeichengSun I'm wrapping up my EiC rotation today and before I had it over to the next EiC I would like to ask if you could please write a few words explaining your intent and availability to resume.

This might weight in the priority assigned to merging this issue: https://github.com/ropensci/statistical-software-review-book/pull/67

cc' @noamross andn @mpadge

As I give way to the next EiC I want to thank you for sharing your work with rOpenSci.

May 04 '25 21:05 maurolepore

Since this issue has been on hold longer than a year, I am going to close it. Please feel free to respond with new information if you'd like it reopened.

Oct 31 '25 19:10 ldecicco-USGS

Sorry @ldecicco-USGS and especially @WeichengSun, the ball is in my court on this one. I'll use your prompt to really endeavour to get the stats standards for network packages moving soon. Are you still interested?

Oct 31 '25 20:10 mpadge

ILSM: Analyze Interconnection Structure of Multilayer Interaction Networks

Scope

Technical checks

Publication options

Code of conduct

Checks for ILSM (v1.0.3.2)

1. rOpenSci Statistical Standards (srr package)

2. Package Dependencies

3. Statistical Properties

3a. Network visualisation

4. goodpractice and other checks

3a. Continuous Integration Badges

3b. goodpractice results

R CMD check with rcmdcheck

Test coverage with covr

Cyclocomplexity with cyclocomp

Static code analyses with lintr

5. Other Checks

Editor-in-Chief Instructions:

1. rOpenSci Statistical Standards (`srr` package)

4. `goodpractice` and other checks

3b. `goodpractice` results

`R CMD check` with rcmdcheck