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galamm: Generalized Additive Latent and Mixed Models #615
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Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type |
🚀 Editor check started 👋 |
Checks for galamm (v0.1.1.9000)git hash: 26cfad15
(Checks marked with 👀 may be optionally addressed.) Package License: GPL (>= 3) 1. rOpenSci Statistical Standards (
|
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
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:
- 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:
- 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)
✖️ 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
👋 @noamross, I just wanted to ask: what's the status of this submission? Is rOpenSci interested in reviewing it? |
@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. |
@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, 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 We'd still like to work with you to get this through our review process. How about one of the following options:
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. |
Thanks @mpadge, I go for option 1 then, and will ping you here once I've got a final decision on the paper. |
@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. |
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: TBD
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which of our statistical package categories this package falls under. (Please check one appropriate box below):
Statistical Packages
Pre-submission Inquiry
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).
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)
Technical checks
Confirm each of the following by checking the box.
autotest
checks on the package, and ensured no tests fail.Running
autotest
gives some errors, but they were waived in the pre-review issue.srr_stats_pre_submit()
function confirms this package may be submitted.pkgcheck()
function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.This package:
Publication options
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.
Code of conduct
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