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Published 20 hours ago • verified publisher icon ropensci

galamm: Generalized Additive Latent and Mixed Models

Open osorensen opened this issue 2 years ago • 58 comments

Submitting Author Name: Øystein Sørensen Submitting Author Github Handle: @osorensen Repository: https://github.com/LCBC-UiO/galamm Version submitted: 0.1.1.9000 Submission type: Stats Badge grade: gold Editor: @noamross Reviewers: @nicholasjclark, @eric-pedersen

Due date for @nicholasjclark: 2024-11-10

Due date for @eric-pedersen: 2025-03-21 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 = "[email protected]",
           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).

Badging

  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.

This package:

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.

osorensen avatar Oct 20 '23 08:10 osorensen

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

ropensci-review-bot avatar Oct 20 '23 08:10 ropensci-review-bot

:rocket:

Editor check started

:wave:

ropensci-review-bot avatar Oct 20 '23 08:10 ropensci-review-bot

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(<path/to/repo>)', 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

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

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

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

Package coverage: 98.35

Cyclocomplexity with 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

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

ropensci-review-bot avatar Oct 20 '23 09:10 ropensci-review-bot

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

osorensen avatar Dec 21 '23 10:12 osorensen

@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.

noamross avatar Dec 21 '23 11:12 noamross

@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?

mpadge avatar Apr 25 '24 07:04 mpadge

@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?

osorensen avatar Apr 25 '24 08:04 osorensen

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

  1. 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,
  2. 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.

mpadge avatar Apr 25 '24 09:04 mpadge

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

osorensen avatar Apr 27 '24 10:04 osorensen

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

  1. 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,
  2. 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.

markean avatar May 03 '24 22:05 markean

@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.

osorensen avatar Aug 14 '24 19:08 osorensen