hdcuremodels #692

kelliejarcher · 2025-03-12T02:01:19Z

Submitting Author Name: Kellie J. Archer
Submitting Author Github Handle: @kelliejarcher
Repository: https://github.com/kelliejarcher/hdcuremodels
Version submitted: 0.0.2
Submission type: Stats
Badge grade: bronze/silver/gold (select one)
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: hdcuremodels
Title: Penalized Mixture Cure Models for High-Dimensional Data
Version: 0.0.2
Date: 2025-03-11
Authors@R: 
    c(person("Han", "Fu", role = "aut"), person(c("Kellie J."), "Archer", email=
    "archer.43@osu.edu", role = c("aut","cre"), comment = c(ORCID="0000-0003-1555-5781")))
Description: Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs. 
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.2.0)
Imports: doParallel,
         flexsurv,
         flexsurvcure,
         foreach,
         ggplot2,
         ggpubr,
         glmnet,
         knockoff,
         mvnfast,
         parallel,
         plyr,
         methods,
         survival
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.2
Suggests: 
    knitr,
    rmarkdown,
    roxygen2,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
LazyData: true
URL: https://github.com/kelliejarcher/hdcuremodels
BugReports: https://github.com/kelliejarcher/hdcuremodels/issues
Config/testthat/edition: 3

Scope

Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):

Statistical Packages
- Bayesian and Monte Carlo Routines
- Dimensionality Reduction, Clustering, and Unsupervised Learning
- Machine Learning
- Regression and Supervised Learning
- Exploratory Data Analysis (EDA) and Summary Statistics
- Spatial Analyses
- Time Series Analyses
- Probability Distributions

Pre-submission Inquiry

A pre-submission inquiry has been approved in issue#690

General Information

Who is the target audience and what are scientific applications of this package?
Analysts who model time-to-event outcomes when some subjects either experience long-term survival or are not susceptible to the event of interest (simplistically, cured).
Paste your responses to our General Standard G1.1 The first implementation of a novel algorithm, describing whether your software is:
- The first implementation of a novel algorithm; or
- The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
- An improvement on other implementations of similar algorithms in R.
Please include hyperlinked references to all other relevant software.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
Not applicable.

Badging

What grade of badge are you aiming for? (silver)
Silver
If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)
Have a demonstrated generality of usage beyond one single envisioned use case.

Technical checks

Confirm each of the following by checking the box.

I have read the rOpenSci packaging guide.
I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
I/we have read the Statistical Software Peer Review Guide for Authors.
I/we have run autotest checks on the package, and ensured no tests fail.
The srr_stats_pre_submit() function confirms this package may be submitted.
The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

does not violate the Terms of Service of any service it interacts with.
has a CRAN and OSI accepted license.
contains a README with instructions for installing the development version.

Publication options

Do you intend for this package to go on CRAN? I submitted 0.0.1 version of hdcuremodels last June and then learned about ROpenSci. I will not submit a new version to CRAN until after the ROpenSci review.
Do you intend for this package to go on Bioconductor?

Code of conduct

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

The text was updated successfully, but these errors were encountered:

ropensci-review-bot · 2025-03-12T02:01:21Z

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

ropensci-review-bot · 2025-03-12T02:01:24Z

🚀

The following problem was found in your submission template:

'statsgrade' variable must be one of [bronze, silver, gold]
Editors: Please ensure these problems with the submission template are rectified. Package checks have been started regardless.

👋

ropensci-review-bot · 2025-03-12T02:37:33Z

Checks for hdcuremodels (v0.0.2)

git hash: c7a555d9

✔️ Package is already on CRAN.
✔️ has a 'codemeta.json' file.
✔️ has a 'contributing' file.
✔️ uses 'roxygen2'.
✔️ 'DESCRIPTION' has a URL field.
✔️ 'DESCRIPTION' has a BugReports field.
✔️ Package has at least one HTML vignette
✔️ All functions have examples.
✔️ Package has continuous integration checks.
✔️ Package coverage is 77.5%.
✔️ R CMD check found no errors.
✔️ R CMD check found no warnings.

Package License: MIT + file LICENSE

1. rOpenSci Statistical Standards (`srr` package)

This package is in the following category:

Regression and Supervised Learning

✔️ All applicable standards [v0.2.0] have been documented in this package (283 complied with; 61 N/A standards)

Click to see the report of author-reported standards compliance of the package with links to associated lines of code, which can be re-generated locally by running the srr_report() function from within a local clone of the repository.

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	1268
internal	stats	281
internal	hdcuremodels	70
internal	graphics	36
internal	utils	19
imports	methods	20
imports	knockoff	13
imports	flexsurv	7
imports	survival	7
imports	glmnet	4
imports	mvnfast	3
imports	parallel	2
imports	flexsurvcure	1
imports	ggpubr	1
imports	doParallel	NA
imports	foreach	NA
imports	ggplot2	NA
imports	plyr	NA
suggests	knitr	NA
suggests	rmarkdown	NA
suggests	roxygen2	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

ncol (90), list (82), rep (79), exp (70), drop (67), c (58), matrix (50), sum (46), log (42), which (42), length (40), is.null (32), if (30), dim (25), data.frame (23), return (20), abs (18), nrow (18), t (17), T (17), cbind (16), max (16), sample (16), gamma (15), sapply (15), replace (14), for (13), names (13), which.max (12), pmax (11), as.numeric (10), seq_len (9), subset (9), apply (8), cumsum (8), match.call (8), mean (8), strsplit (8), substitute (8), colSums (7), F (7), pmin (7), which.min (7), ifelse (6), parse (6), paste (6), seq_along (6), summary (6), attr (5), diag (5), eval (5), grep (5), merge (5), rbind (5), sqrt (5), as.character (4), as.data.frame (4), as.list (4), call (4), dimnames (4), match (4), nchar (4), numeric (4), order (4), parent.frame (4), rowMeans (4), substr (4), trimws (4), unique (4), missing (3), rank (3), round (3), rowSums (3), table (3), try (3), as.vector (2), by (2), colMeans (2), diff (2), sort (2), unname (2), choose (1), colnames (1), environment (1), expand.grid (1), gsub (1), warning (1)

stats

time (115), coef (24), optim (16), AIC (13), uniroot (13), df (12), BIC (11), sigma (9), sd (7), step (6), family (5), formula (5), as.formula (4), model.matrix (4), model.response (4), offset (4), rnorm (4), var (4), aggregate (3), glm (3), rexp (3), dist (2), knots (2), model.frame (2), runif (2), rbinom (1), rweibull (1), splinefun (1), terms (1)

hdcuremodels

self_scale (16), l1_negloglik_inc (6), cure_estimate (3), exp_cure (3), exp_negloglik_lat (3), mcp_scad_negloglik_inc (3), weib.cure.negloglik (3), AUC_msi (2), cox_l1 (2), cure.em (2), exp_negloglik (2), extract_rhs_values (2), get_cox_lambda_max (2), select_model (2), auc_mcm (1), C.stat (1), concordance_mcm (1), cureem (1), curegmifs (1), cv_cureem (1), cv_curegmifs (1), cv.em.fdr (1), cv.em.inner (1), cv.em.nofdr (1), cv.gmifs.fdr (1), cv.gmifs.inner (1), cv.gmifs.nofdr (1), exp_update (1), generate_cure_data (1), mcp_penalty (1), mcp_scad_negloglik_lat (1), sim_cure (1), weib.cure.update (1)

graphics

par (19), text (14), frame (3)

methods

is (20)

utils

data (19)

knockoff

create.second_order (7), knockoff.threshold (6)

flexsurv

pgengamma (5), rgompertz (2)

survival

coxph (3), survfit (2), Surv (1), survreg (1)

glmnet

glmnet (4)

mvnfast

rmvn (3)

parallel

makeCluster (2)

flexsurvcure

flexsurvcure (1)

ggpubr

ggarrange (1)

NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately.

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 18 files) and
2 authors
1 vignette
2 internal data files
13 imported packages
15 exported functions (median 96 lines of code)
117 non-exported functions in R (median 32 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	18	76.8
files_vignettes	1	61.7
files_tests	18	93.9
loc_R	3786	91.7
loc_vignettes	276	58.3
loc_tests	1088	85.0
num_vignettes	1	58.7
data_size_total	842035	93.9
data_size_median	421017	96.6	TRUE
n_fns_r	132	81.1
n_fns_r_exported	15	58.5
n_fns_r_not_exported	117	84.9
n_fns_per_file_r	4	64.0
num_params_per_fn	4	51.1
loc_per_fn_r	36	82.2
loc_per_fn_r_exp	96	91.5
loc_per_fn_r_not_exp	32	80.2
rel_whitespace_R	3	52.6
rel_whitespace_vignettes	36	60.5
rel_whitespace_tests	6	60.8
doclines_per_fn_exp	51	63.8
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	101	78.4

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
13799638498	pkgcheck	success	c7a555	8	2025-03-11
13799638500	R-CMD-check.yaml	success	c7a555	5	2025-03-11

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fails:

cyclocomp
no_description_date
no_import_package_as_a_whole

Test coverage with covr

Package coverage: 77.51

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
cv_cureem	57
coef.mixturecure	52
plot.mixturecure	46
cureem	43
cv.em.nofdr	41
cv_curegmifs	39
concordance_mcm	37
curegmifs	37
inits_check	28
cv.gmifs.nofdr	26
cox_l1	25
cox_mcp_scad	24
predict.mixturecure	24
select_model	24
nonzerocure_test	22
C.stat	17
cure.em	17
exp_EM	17
weib_EM	17
generate_cure_data	15

Static code analyses with lintr

lintr found the following 585 potential issues:

message	number of times
Avoid 1:length(...) expressions, use seq_len.	2
Avoid 1:ncol(...) expressions, use seq_len.	19
Avoid 1:nrow(...) expressions, use seq_len.	1
Avoid library() and require() calls in packages	2
Avoid using sapply, consider vapply instead, that's type safe	11
Lines should not be more than 80 characters. This line is 100 characters.	4
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Use <-, not =, for assignment.	7

Package Versions

package	version
pkgstats	0.2.0.54
pkgcheck	0.1.2.122
srr	0.1.3.26

Editor-in-Chief Instructions:

This package is in top shape and may be passed on to a handling editor

maurolepore · 2025-03-20T13:37:19Z

@kelliejarcher thanks so much for following up with a full submission.

Here I share some preliminary checks to support conversations with potential handling editors. If any of this feedback feels useful, now it's a great time to incorporate it.

We'll come back ASAP.

EiC pre-checks

Documentation: The package has sufficient documentation available online (README, pkgdown docs) to allow for an assessment of functionality and scope without installing the package. In particular,

Is the case for the package well made?
Is the reference index page clear (grouped by topic if necessary)?

Lacks a pkgdown website.
The file names in R/ suggest an alpha sorted reference might not group functionality in a meaningful way.

Are vignettes readable, sufficiently detailed and not just perfunctory?

Consider renaming the file to match the package name so that pkgdown automatically adds a "Get started" tab on the website: mv vignettes/hdcuremodels-vignette.Rmd vignettes/hdcuremodels.Rmd

Fit: The package meets criteria for fit and overlap.
Installation instructions: Are installation instructions clear enough for human users?

Consider adding instructions to install the development version

Tests: If the package has some interactivity / HTTP / plot production etc. are the tests using state-of-the-art tooling?

I see set.seed() and no cleanup. Consider using withr::local_seed() for auto-cleanup.
I see multiple calls to expect_error() with no string, class, or snapshot to match. Consider making the expectation more specific.

Contributing information: Is the documentation for contribution clear enough e.g. tokens for tests, playgrounds?
License: The package has a CRAN or OSI accepted license.
Project management: Are the issue and PR trackers in a good shape, e.g. are there outstanding bugs, is it clear when feature requests are meant to be tackled?

kelliejarcher · 2025-04-22T18:42:11Z

This is my first ROpenSci submission. I previously made some revisions based on comments below. Can you please explain what the next steps will be? Thanks for any information, Kellie

maurolepore · 2025-04-24T01:17:35Z

@kelliejarcher thanks for following up. I re-announced the package among our editors. Once we find an available handling editor they will guide you through the rest of the process.

BTW, ideally, to keep this thread easier to follow it's best to try respond directly through the GitHub interface. Responding to the email notification duplicates the message you're responding to 😸

maurolepore · 2025-05-04T22:07:00Z

Here I mark the end of my EiC rotation and leave a short note for the next EiC.

This is a stats submission.
The package was announced twice but still lacks a handling editor.

ropensci-review-bot added the holding label Mar 12, 2025

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hdcuremodels #692

hdcuremodels #692

kelliejarcher commented Mar 12, 2025 •

edited by maurolepore

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3a. Continuous Integration Badges

3b. `goodpractice` results

`R CMD check` with rcmdcheck

Test coverage with covr

Cyclocomplexity with cyclocomp

Static code analyses with lintr

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maurolepore commented Mar 20, 2025 •

edited

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kelliejarcher commented Apr 22, 2025 via email •

edited by maurolepore

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hdcuremodels #692

hdcuremodels #692

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kelliejarcher commented Mar 12, 2025 • edited by maurolepore Loading Uh oh! There was an error while loading. Please reload this page.

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Scope

Pre-submission Inquiry

General Information

Badging

Technical checks

Publication options

Code of conduct

ropensci-review-bot commented Mar 12, 2025

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ropensci-review-bot commented Mar 12, 2025

Checks for hdcuremodels (v0.0.2)

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

Editor-in-Chief Instructions:

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maurolepore commented Mar 20, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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EiC pre-checks

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kelliejarcher commented Apr 22, 2025 via email • edited by maurolepore Loading Uh oh! There was an error while loading. Please reload this page.

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kelliejarcher commented Mar 12, 2025 •

edited by maurolepore

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1. rOpenSci Statistical Standards (`srr` package)

4. `goodpractice` and other checks

3b. `goodpractice` results

`R CMD check` with rcmdcheck

maurolepore commented Mar 20, 2025 •

edited

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kelliejarcher commented Apr 22, 2025 via email •

edited by maurolepore

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