galamm: Generalized Additive Latent and Mixed Models

osorensen commented 1 year ago

Submitting Author Name: Øystein Sørensen Submitting Author Github Handle: !--author1-->@osorensen@noamross<!--end-editor-- Reviewers: @nicholasjclark, @eric-pedersen

Due date for @nicholasjclark: 2024-11-10 Due date for @eric-pedersen: 2024-11-23

Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: galamm
Title: Generalized Additive Latent and Mixed Models
Version: 0.1.1.9000
Authors@R: c(
    person(given = "Øystein",
           family = "Sørensen",
           role = c("aut", "cre"),
           email = "oystein.sorensen@psykologi.uio.no",
           comment = c(ORCID = "0000-0003-0724-3542")),
    person(given = "Douglas", family = "Bates", role = "ctb"),       
    person(given = "Ben", family = "Bolker", role = "ctb"),
    person(given = "Martin", family = "Maechler", role = "ctb"),
    person(given = "Allan", family = "Leal", role = "ctb"),
    person(given = "Fabian", family = "Scheipl", role = "ctb"),
    person(given = "Steven", family = "Walker", role = "ctb"),
    person(given = "Simon", family = "Wood", role = "ctb")
           )
Description: Estimates generalized additive latent and
    mixed models using maximum marginal likelihood, 
    as defined in Sorensen et al. (2023) 
    <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
    Skrondal (2004)'s unifying framework for multilevel latent variable 
    modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse 
    matrix methods, Laplace approximation, and automatic differentiation. The 
    framework includes generalized multilevel models with heteroscedastic 
    residuals, mixed response types, factor loadings, smoothing splines, 
    crossed random effects, and combinations thereof. Syntax for model 
    formulation is close to 'lme4' (Bates et al. (2015) 
    <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) 
    <doi:10.1080/00273171.2018.1516541>).
License: GPL (>= 3)
URL: https://github.com/LCBC-UiO/galamm, https://lcbc-uio.github.io/galamm/
BugReports: https://github.com/LCBC-UiO/galamm/issues
Encoding: UTF-8
Imports: 
    lme4,
    Matrix,
    memoise,
    methods,
    mgcv,
    nlme,
    Rcpp,
    Rdpack,
    stats
Depends:
    R (>= 3.5.0)
LinkingTo:
    Rcpp,
    RcppEigen
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
Suggests:
    covr,
    gamm4,
    knitr,
    PLmixed,
    rmarkdown,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr, rmarkdown
RdMacros: Rdpack
NeedsCompilation: yes
SystemRequirements: C++17

Scope

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

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

Pre-submission Inquiry

[x] A pre-submission inquiry has been approved in issue 614

General Information

Who is the target audience and what are scientific applications of this package? The target audience is applied statisticians and quantitative scientists, particularly those working on the social sciences. The package is motivated by longitudinal studies in cognitive neuroscience, but it is applicable wherever a measurement model (of factor analysis type) needs to be combined with hierarchical modeling.
Paste your responses to our General Standard G1.1 here, describing whether your software is:

This is the first implementation of the algorithm developed in Sørensen, Fjell, and Walhovd (2023).

(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? (bronze, silver, gold) gold
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)
1. "Compliance with a good number of standards beyond those identified as minimally necessary.": I have attempted to comply with all standards for regression software outlined in the Online Book for Statistical Software. I have used srr to point out which parts of the code I think address each of the standards.
2. "Demonstrating excellence in compliance with multiple standards from at least two broad sub-categories.": I have tried to comply with all the standards in 6.1.1 - 6.1.5 of the Standards Chapter.
3. "Have a demonstrated generality of usage beyond one single envisioned use case.": The software supports generality of usage, and the vignettes describe several such use cases.

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 have another maintainer identified.
[x] 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. Running autotest gives some errors, but they were waived in the pre-review issue.
[x] The srr_stats_pre_submit() function confirms this package may be submitted.
[x] 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:

[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.

Publication options

[x] Do you intend for this package to go on CRAN? The package is on CRAN. I am aware that rOpenSci recommends waiting with submitting to CRAN, but the package has some users already, and having pre-compiled binaries on CRAN makes it easier for them to install it, rather than having to set up a toolchain required for install from source. I hence opted to send it to CRAN.
[ ] Do you intend for this package to go on Bioconductor?

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.

ropensci-review-bot commented 1 year ago

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

ropensci-review-bot commented 1 year ago

:rocket:

Editor check started

:wave:

ropensci-review-bot commented 1 year ago

Checks for galamm (v0.1.1.9000)

git hash: 26cfad15

:heavy_check_mark: Package is already 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 98.4%.
: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

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

Package License: GPL (>= 3)

1. rOpenSci Statistical Standards (`srr` package)

:heavy_check_mark: All applicable standards [v0.2.0] have been documented in this package (217 complied with; 0 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 | 439| |internal |galamm | 76| |internal |utils | 30| |internal |graphics | 6| |imports |stats | 79| |imports |lme4 | 11| |imports |Matrix | 11| |imports |mgcv | 8| |imports |methods | 3| |imports |nlme | 3| |imports |memoise | 1| |imports |Rcpp | NA| |imports |Rdpack | NA| |suggests |covr | NA| |suggests |gamm4 | NA| |suggests |knitr | NA| |suggests |PLmixed | NA| |suggests |rmarkdown | NA| |suggests |testthat | NA| |linking_to |Rcpp | NA| |linking_to |RcppEigen | 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()', and examining the 'external_calls' table.

base

list (31), seq_along (22), for (19), lapply (19), length (18), c (17), names (15), ncol (15), attr (14), seq_len (14), vapply (14), if (13), drop (11), rep (11), as.numeric (10), is.null (9), nrow (9), integer (8), unlist (8), factor (7), paste (7), qr (7), diff (6), max (6), seq (6), all.vars (5), any (5), matrix (5), numeric (5), cbind (4), colnames (4), logical (4), sqrt (4), beta (3), eval (3), grepl (3), levels (3), Map (3), match.call (3), Reduce (3), return (3), row.names (3), scale (3), unique (3), by (2), col (2), data.frame (2), diag (2), do.call (2), ifelse (2), lengths (2), order (2), parent.frame (2), qr.R (2), rank (2), rbind (2), abs (1), array (1), as.character (1), as.integer (1), as.logical (1), as.matrix (1), assign (1), backsolve (1), deparse (1), deparse1 (1), dim (1), environment (1), getOption (1), inherits (1), intersect (1), is.infinite (1), is.nan (1), min (1), parse (1), pmax (1), qr.qty (1), regexpr (1), rep.int (1), rowSums (1), setdiff (1), split (1), sum (1), t (1), tabulate (1), which (1)

stats

deviance (9), pf (8), formula (6), as.formula (4), BIC (4), family (4), logLik (4), model.matrix (4), weights (4), quantile (3), terms (3), AIC (2), nobs (2), rf (2), terms.formula (2), contrasts (1), D (1), delete.response (1), df (1), gaussian (1), getCall (1), model.frame (1), model.response (1), na.action (1), optim (1), pchisq (1), pnorm (1), qnorm (1), reformulate (1), smooth (1), start (1), update (1), vcov (1)

galamm

extractor (3), factor_finder (3), find_parm_inds (3), fn (3), gr (3), mlwrapper (3), define_factor_mappings (2), extend_lambda (2), extract_name (2), find_k (2), gam.setup (2), gamm4 (2), gamm4.setup (2), interpret.gam0 (2), set_initial_values (2), setup_factor (2), anova.galamm (1), coef.galamm (1), confint.galamm (1), deviance.galamm (1), extract_optim_parameters (1), extract_optim_parameters.galamm (1), factor_loadings (1), factor_loadings.galamm (1), family.galamm (1), fitted.galamm (1), fixef.galamm (1), formula.galamm (1), galamm (1), galamm_control (1), gam.side (1), gamm4.wrapup (1), llikAIC (1), logLik.galamm (1), mappingunwrapping (1), marginal_likelihood (1), new_galamm_control (1), nobs.galamm (1), plot_smooth (1), plot_smooth.galamm (1), plot.galamm (1), predict.galamm (1), print.summary.galamm (1), print.VarCorr.galamm (1), ranef.galamm (1), release_questions (1), residuals.galamm (1), setup_family (1), setup_response_object (1), sl (1), squeeze_mappings (1), t2l (1), VarCorr.galamm (1), variable.summary (1)

utils

data (30)

lme4

findbars (3), nobars (3), lFormula (2), mkReTrms (2), .prt.VC (1)

Matrix

t (4), chol (2), Matrix (2), solve (2), Diagonal (1)

mgcv

new.name (2), smooth2random (2), Rrank (1), s (1), smoothCon (1), t2 (1)

graphics

par (3), abline (2), text (1)

methods

as (3)

nlme

fixef (1), ranef (1), VarCorr (1)

memoise

memoise (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 C++ (4% in 2 files), C/C++ Header (66% in 18 files) and R (29% in 30 files) - 1 authors - 9 vignettes - 8 internal data files - 9 imported packages - 31 exported functions (median 6 lines of code) - 81 non-exported functions in R (median 16 lines of code) - 618 C/C++ functions (median 4 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](https://docs.ropensci.org/pkgcheck/reference/checks_to_markdown.html) 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 | 30| 89.3| | |files_src | 2| 79.1| | |files_inst | 18| 99.6| | |files_vignettes | 9| 99.2| | |files_tests | 10| 90.7| | |loc_R | 1777| 81.8| | |loc_src | 252| 31.9| | |loc_inst | 4014| 86.1| | |loc_vignettes | 1732| 96.3|TRUE | |loc_tests | 2479| 95.4|TRUE | |num_vignettes | 9| 99.6|TRUE | |data_size_total | 265405| 88.8| | |data_size_median | 13688| 80.9| | |n_fns_r | 112| 79.1| | |n_fns_r_exported | 31| 79.2| | |n_fns_r_not_exported | 81| 79.5| | |n_fns_src | 618| 96.1|TRUE | |n_fns_per_file_r | 2| 39.7| | |n_fns_per_file_src | 24| 95.1|TRUE | |num_params_per_fn | 2| 11.9| | |loc_per_fn_r | 12| 36.1| | |loc_per_fn_r_exp | 6| 10.5| | |loc_per_fn_r_not_exp | 16| 52.7| | |loc_per_fn_src | 4| 2.0|TRUE | |rel_whitespace_R | 18| 80.9| | |rel_whitespace_src | 14| 29.1| | |rel_whitespace_inst | 24| 85.7| | |rel_whitespace_vignettes | 51| 99.2|TRUE | |rel_whitespace_tests | 11| 88.9| | |doclines_per_fn_exp | 42| 52.8| | |doclines_per_fn_not_exp | 0| 0.0|TRUE | |fn_call_network_size | 1302| 98.5|TRUE | ---

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 [![R-CMD-check.yaml](https://github.com/LCBC-UiO/galamm/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/LCBC-UiO/galamm/actions) **GitHub Workflow Results** | id|name |conclusion |sha | run_number|date | |----------:|:--------------------------|:----------|:------|----------:|:----------| | 6584930507|lint |success |26cfad | 495|2023-10-20 | | 6584967181|pages build and deployment |success |2fba91 | 144|2023-10-20 | | 6584930514|pkgdown |success |26cfad | 350|2023-10-20 | | 6584930510|R-CMD-check |success |26cfad | 558|2023-10-20 | | 6584930523|test-coverage |success |26cfad | 248|2023-10-20 | --- #### 3b. `goodpractice` results #### `R CMD check` with [rcmdcheck](https://r-lib.github.io/rcmdcheck/) R CMD check generated the following note: 1. checking installed package size ... NOTE installed size is 34.0Mb sub-directories of 1Mb or more: doc 2.0Mb libs 30.7Mb R CMD check generated the following check_fail: 1. rcmdcheck_reasonable_installed_size #### Test coverage with [covr](https://covr.r-lib.org/) Package coverage: 98.35 #### Cyclocomplexity with [cyclocomp](https://github.com/MangoTheCat/cyclocomp) The following functions have cyclocomplexity >= 15: function | cyclocomplexity --- | --- galamm | 45 gam.setup | 44 gamm4.wrapup | 44 interpret.gam0 | 29 define_factor_mappings | 17 galamm_control | 17 #### Static code analyses with [lintr](https://github.com/jimhester/lintr) [lintr](https://github.com/jimhester/lintr) found the following 296 potential issues: message | number of times --- | --- Avoid library() and require() calls in packages | 10 Lines should not be more than 80 characters. | 286

5. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 2 function names are duplicated in other packages: - - `plot_smooth` from itsadug - - `sl` from reinsureR

Package Versions

|package |version | |:--------|:---------| |pkgstats |0.1.3.9 | |pkgcheck |0.1.2.10 | |srr |0.0.1.194 |

Editor-in-Chief Instructions:

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

osorensen commented 11 months ago

👋 @noamross, I just wanted to ask: what's the status of this submission? Is rOpenSci interested in reviewing it?

noamross commented 11 months ago

@osorensen, thanks for following up. My apologies, I think this package fell between cracks in our editor hand-off. I'll follow up later today.

mpadge commented 7 months ago

@osorensen Apologies once again, with recent organisational changes this once again fell through the cracks. We are now finally on it. How are you positioned if we finally get the process started now?

osorensen commented 7 months ago

@mpadge, a paper describing the package is currently under review for a journal, so I think my best option now is the withdraw the submission to ropensci. I can maybe just to that by closing this issue?

mpadge commented 7 months ago

@osorensen We'd still like to work with you to get this through our review process. How about one of the following options:

We put the submission on hold here, until you let us know when your paper has passed through review. We'll then re-start the official review process straight after then. Or,
We start the review process anyway. Our reviews are generally completed within just a few months at most. Especially given our slow response thus far, we'd ensure that our process would be completed well before a typical manuscript review. (Some good examples for recent stats submissions are #550 and #571, both done in around 2 months.) All changes and software improvements during our review could be used to support your responses to manuscript reviews.

Note that if your submission is to Journal of Statistical Software, then our system has been developed in collaboration with their processes, and they would likely welcome you using the results of a review here to support their own process.

osorensen commented 7 months ago

Thanks @mpadge, I go for option 1 then, and will ping you here once I've got a final decision on the paper.

markean commented 6 months ago

@osorensen We'd still like to work with you to get this through our review process. How about one of the following options:

We put the submission on hold here, until you let us know when your paper has passed through review. We'll then re-start the official review process straight after then. Or,

We start the review process anyway. Our reviews are generally completed within just a few months at most. Especially given our slow response thus far, we'd ensure that our process would be completed well before a typical manuscript review. (Some good examples for recent stats submissions are Submission - melt: Multiple Empirical Likelihood Tests #550 and waywiser: Ergonomic Methods for Assessing Spatial Models #571, both done in around 2 months.) All changes and software improvements during our review could be used to support your responses to manuscript reviews.

Note that if your submission is to Journal of Statistical Software, then our system has been developed in collaboration with their processes, and they would likely welcome you using the results of a review here to support their own process.

@mpadge, I just stumbled upon this notification. Thank you for mentioning my package review case. The peer review process has significantly enhanced the package's quality in a very short period of time, and I also believe this has expedited its review for publication in the Journal of Statistical Software.

osorensen commented 3 months ago

@mpadge, the software paper is now published in Multivariate Behavioral Research, https://doi.org/10.1080/00273171.2024.2385336. We can therefore start this review now.

adamhsparks commented 3 months ago

@ropensci-review-bot check srr

adamhsparks commented 3 months ago

@ropensci-review-bot check srr

maelle commented 2 months ago

@ropensci-review-bot check srr

adamhsparks commented 2 months ago

@ropensci-review-bot check srr

mpadge commented 2 months ago

@adamhsparks @osorensen Sorry for any inconvenience. The non-responsive bot was a bug on our side that has now been fixed. It should all work now when you call check srr again.

maelle commented 2 months ago

@ropensci-review-bot check srr

ropensci-review-bot commented 2 months ago

:heavy_multiplication_x: Error: Package documents compliance only with general standards. Statistical packages must document compliance with at least one set of category-specific standards as well.

The following standards are missing:

General standards:

G5.2, G5.2a, G5.2b, G5.3, G5.4a, G5.4b, G5.5, G5.6

All standards must be documented prior to submission

osorensen commented 2 months ago

@maelle, the above seems to be a false positive. The mentioned standards are documented here:

https://github.com/LCBC-UiO/galamm/blob/578b52c8a7db11401d98f12a2c21b7fdccc3e37f/tests/testthat.R#L1-L25

adamhsparks commented 2 months ago

@osorensen, unfortunately, that's not a false positive. They are not properly documented so that the bot can find the standards.

The documentation for ROxygen2 needs to be in /R, not /tests for it to be valid, e.g., https://github.com/giovsaraceno/QuadratiK-package/blob/master/R/srr-stats-standards.R

If you just move it, and redocument it should be fine.

osorensen commented 2 months ago

@ropensci-review-bot check srr

mpadge commented 2 months ago

@osorensen Sorry, bot's not quite ready for your case. Yours is the first package we've had with standards in tests/testthat.R. That is of course perfectly okay, but we simply hadn't encountered it, and so didn't realise that it caused a failure on our side. I'll ping here when our system has been rebuilt with the fix.

(And FYI @adamhsparks, "srrstats" tags can go pretty much anywhere at all, including README, tests, src, and R code.)

ropensci-review-bot commented 2 months ago

:heavy_multiplication_x: Error: Package documents compliance only with general standards. Statistical packages must document compliance with at least one set of category-specific standards as well.

'srr' standards compliance:

Complied with: 68 / 68 = 100% (general: 68 / 68)
Not complied with: / = % (

:heavy_check_mark: This package complies with > 50% of all standads and may be submitted.

osorensen commented 2 months ago

@mpadge, I moved the documentation to R/, so hope it's fine now.

adamhsparks commented 2 months ago

@osorensen Sorry, bot's not quite ready for your case. Yours is the first package we've had with standards in tests/testthat.R. That is of course perfectly okay, but we simply hadn't encountered it, and so didn't realise that it caused a failure on our side. I'll ping here when our system has been rebuilt with the fix.

(And FYI @adamhsparks, "srrstats" tags can go pretty much anywhere at all, including README, tests, src, and R code.)

Sorry, my bad. I was just trying to help based on previous cases.

mpadge commented 2 months ago

@adamhsparks and @osorensen The system has now been rebuilt, and the bot comment above that "This package ... may be submitted" would no longer appear. The first "Error:" would first have to be addressed.

osorensen commented 2 months ago

@adamhsparks and @osorensen The system has now been rebuilt, and the bot comment above that "This package ... may be submitted" would no longer appear. The first "Error:" would first have to be addressed.

@mpadge, this seemed to be fine when I submitted the package, see the srr check in this comment. Have the standards changed since October 2023?

mpadge commented 2 months ago

@osorensen The standards haven't changed, but since that time we've added additional checks to identify packages which only document general standards, with no category-specific ones. The general package checks also now generate and link to the full report on stats standards compliance, like in this example here, so we'd explicitly see there also that compliance statements with regression standards were missing.

So before proceeding, we'd need you to comply with our Regression Standards. Previous experience suggests that'll require around one day's work or so. As always, please let us know if you've got any questions, and in particular, feel free to comment in any way you like regarding the standards, ideally in the stats-devguide repo, linked directly from the book. Thanks!

osorensen commented 2 months ago

@ropensci-review-bot check srr

osorensen commented 2 months ago

@mpadge @adamhsparks, I'm not sure if I'm allowed to run check srr myself, but I've added tags which confirm that the package complies with the regression standards now. They are all in the main branch of my repository. Could you please check if this is sufficient?

mpadge commented 2 months ago

@osorensen Sorry, that was a wee :bug: on our side. Should work now, but note that result will currently indicate that RE4.15 is missing, so maybe add that standard first before calling?

osorensen commented 2 months ago

@ropensci-review-bot check srr

osorensen commented 2 months ago

Thanks @mpadge, there was a typo in there, which should be fixed now. I tried running check srr above but not sure if anything happens.

adamhsparks commented 2 months ago

@ropensci-review-bot check srr

mpadge commented 2 months ago

@ropensci-review-bot check srr

ropensci-review-bot commented 2 months ago

'srr' standards compliance:

Complied with: 117 / 117 = 100% (general: 68 / 68; regression: 49 / 49)

:heavy_check_mark: This package complies with > 50% of all standards and may be submitted.

mpadge commented 2 months ago

@osorensen @adamhsparks The previous calls seem to have issued during our weekly system rebuild. Just rotten timing, but all should work fine from here on. And nice work @osorensen - 100% compliance!!

adamhsparks commented 2 months ago

@ropensci-review-bot assign @noamross as editor

ropensci-review-bot commented 2 months ago

Assigned! @noamross is now the editor

noamross commented 2 months ago

@ropensci-review-bot seeking reviewers

ropensci-review-bot commented 2 months ago

Please add this badge to the README of your package repository:

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noamross commented 1 month ago

@ropensci-review-bot assign @nicholasjclark as reviewer

ropensci-review-bot commented 1 month ago

@nicholasjclark added to the reviewers list. Review due date is 2024-10-31. Thanks @nicholasjclark for accepting to review! Please refer to our reviewer guide.

rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers guide and code of conduct for more.

ropensci-review-bot commented 1 month ago

@nicholasjclark: If you haven't done so, please fill this form for us to update our reviewers records.

noamross commented 1 month ago

@ropensci-review-bot set due date for @nicholasjclark to 2024-11-10

ropensci-review-bot commented 1 month ago

Review due date for @nicholasjclark is now 10-November-2024

noamross commented 4 weeks ago

@ropensci-review-bot assign @eric-pedersen as reviewer

ropensci-review-bot commented 4 weeks ago

@eric-pedersen added to the reviewers list. Review due date is 2024-11-16. Thanks @eric-pedersen for accepting to review! Please refer to our reviewer guide.

rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers guide and code of conduct for more.

noamross commented 4 weeks ago

@ropensci-review-bot set due date for @eric-pedersen to 2024-11-23

ropensci-review-bot commented 4 weeks ago

Review due date for @eric-pedersen is now 23-November-2024

ropensci / software-review