dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

@santikka

Date accepted: 2022-12-13
Submitting Author Name: Santtu Tikka
Submitting Author Github Handle: @santikka
Other Package Authors Github handles: @helske
Repository: https://github.com/santikka/dynamite
Version submitted: 0.0.1
Submission type: Stats
Badge grade: gold
Editor: @noamross
Reviewers: @nicholasjclark, @LucyMcGowan

Due date for @nicholasjclark: 2022-09-27

Due date for @LucyMcGowan: 2022-10-20
Archive: TBD
Version accepted: TBD
Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: dynamite
Title: Bayesian Modeling and Causal Inference for Multivariate
    Longitudinal Data
Version: 0.0.1
Authors@R: c(
    person("Santtu", "Tikka", , "santtuth@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-4039-4342")),
    person("Jouni", "Helske", , "jouni.helske@iki.fi", role = "aut",
           comment = c(ORCID = "0000-0001-7130-793X"))
  )
Description: Easy-to-use and efficient interface for 
  Bayesian inference of complex panel (time series) data. The package supports 
  joint modeling of multiple measurements per individual, time-varying and
  time-invariant effects, and a wide range of discrete and 
  continuous distributions. Estimation of the models is carried out via 'Stan'.
License: GPL (>= 3)
URL: https://github.com/santikka/dynamite
BugReports: https://github.com/santikka/dynamite/issues
Depends: 
    R (>= 4.1.0)
Imports: 
    bayesplot,
    checkmate,
    cli,
    data.table (>= 1.14.3),
    dplyr,
    glue,
    ggplot2,
    MASS,
    posterior,
    rlang,
    rstan (>= 2.26.11),
    stats,
    tidyr,
    utils
Suggests: 
    covr,
    knitr,
    plm,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd",
    "srr::srr_stats_roclet"))
RoxygenNote: 7.2.1
LazyData: true
LazyDataCompression: xz

Pre-submission Inquiry

A pre-submission inquiry has been approved in issue #552

General Information

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

The package is mainly intended for applied researchers working with complex panel data.
Panel data is common in many scientific fields, especially in sociology and econometrics. For example, analysing individual-level life-course data is valuable for assessing the effects of policy reforms and other interventions.

Paste your responses to our General Standard G1.1 here, describing whether your software is:

The dynamite R package provides easy-to-use interface for Bayesian inference of complex panel (time series) data comprising of multiple measurements per multiple individuals measured in time. The main features distinguishing the package and the underlying methodology from many other approaches are:

Support for both time-invariant and time-varying effects modeled via B-splines.
Joint modeling of multiple measurements per individual (multiple channels) based directly on the assumed data generating process.
Support for non-Gaussian observations: Currently Gaussian, Categorical, Poisson, Bernoulli, Binomial, Negative Binomial, Gamma, Exponential, and Beta distributions are available and these can be mixed arbitrarily in multichannel models.
Allows evaluating realistic long-term counterfactual predictions which take into account the dynamic structure of the model by posterior predictive distribution simulation.
Transparent quantification of parameter and predictive uncertainty due to a fully Bayesian approach.
User-friendly and efficient R interface with state-of-the-art estimation via Stan.

There are several R packages in CRAN focusing on panel data analysis including but not limited to:

plm for linear panel data models.
fixest for fixed effects and different distributions of response variables (based on stats::family).
panelr for "within-between" models which combine fixed effect and random effect models.
lavaan for general structural equation modelling, and thus can be used to estimate various panel data models such as cross-lagged panel models with fixed or random intercepts.

However, to the best of our knowledge, there are no other R packages (or software in general) that support all features of dynamite simultaneously. Thus The first implementation of a novel algorithm seems most applicable.

(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)

We designed dynamite from the ground up with the standards in mind and thus we think the package fulfills all four aspects. We have been able to comply with 129 standards with only 42 N/A standards across the "Bayesian and Monte Carlo" and "Regression and Supervised Learning" categories. The package is very general and capable of supporting a wide range of data and model structures as demonstrated by the examples and the package tests. In our opinion, the internal structure of the package is also well motivated and compartmentalized. We have also tried to carefully select the package dependencies and keep them to a minimum.

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.
autotest reports failures for the following tests:

    type       test_name        fn_name          parameter parameter_type   operation                      content         
   <chr>      <chr>            <chr>            <chr>     <chr>            <chr>                          <chr>            
 1 diagnostic single_char_case get_code         time      single character upper-case character parameter is case dependent
 2 diagnostic single_char_case get_code         group     single character upper-case character parameter is case dependent
 3 diagnostic single_char_case get_data         time      single character upper-case character parameter is case dependent
 4 diagnostic single_char_case get_data         group     single character upper-case character parameter is case dependent
 5 diagnostic single_char_case get_priors       time      single character upper-case character parameter is case dependent
 6 diagnostic single_char_case get_priors       group     single character upper-case character parameter is case dependent
 7 message    NA               mcmc_diagnostics NA        NA               normal function call           0 of 400 iterati…
 8 message    NA               mcmc_diagnostics NA        NA               normal function call           0 of 400 iterati…
 9 message    NA               mcmc_diagnostics NA        NA               normal function call           E-BFMI indicated…
10 message    NA               mcmc_diagnostics NA        NA               NA                             0 of 400 iterati…
11 message    NA               mcmc_diagnostics NA        NA               NA                             0 of 400 iterati…
12 message    NA               mcmc_diagnostics NA        NA               NA                             E-BFMI indicated…

The first six of these report that the time and group parameters of several functions are case dependent. This is by design, as both of these parameters correspond to column names of the input data. While not entirely sure, we believe the remaining failures occur because the function mcmc_diagnostic produces messages while not being a print or a summary method, but this is also by design as the function outputs diagnostic messages related to the MCMC runs. The output of autotest is not very clear on this matter, as all columns are just filled with NA values.

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?
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.

@ropensci-review-bot

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

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Checks for dynamite (v0.0.1)

git hash: a8d932ca

✔️ Package name is available
✔️ has a 'codemeta.json' file.
✔️ has a 'contributing' file.
✖️ The following functions have no documented return values: [coef.dynamitefit, dynamite, fitted.dynamitefit, formula.dynamitefit, plot_nus, +.dynamiteformula, predict.dynamitefit, print.dynamitefit, print.dynamiteformula, summary.dynamitefit]
✔️ uses 'roxygen2'.
✔️ 'DESCRIPTION' has a URL field.
✔️ 'DESCRIPTION' has a BugReports field.
✔️ Package has at least one HTML vignette
✔️ All functions have examples.
✖️ Function names are duplicated in other packages
✔️ Package has continuous integration checks.
✔️ Package coverage is 97.8%.
✔️ R CMD check found no errors.
✔️ R CMD check found no warnings.

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

Package License: GPL (>= 3)

1. rOpenSci Statistical Standards (`srr` package)

This package is in the following category:

Bayesian and Monte Carlo
Regression and Supervised Learning

✔️ All applicable standards [v0.1.0] have been documented in this package (129 complied with; 42 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	1102
internal	dynamite	418
internal	graphics	21
internal	methods	3
imports	utils	81
imports	stats	59
imports	dplyr	29
imports	rlang	16
imports	checkmate	11
imports	glue	11
imports	cli	6
imports	ggplot2	4
imports	posterior	4
imports	tidyr	4
imports	rstan	3
imports	data.table	2
imports	bayesplot	1
imports	MASS	NA
suggests	covr	NA
suggests	knitr	NA
suggests	plm	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 (108), list (96), length (69), paste0 (66), args (53), for (47), attr (44), data.frame (39), as.list (32), match.call (32), rep (31), seq_len (25), unique (25), do.call (22), character (19), which (18), vapply (17), is.na (16), names (16), logical (15), seq_along (15), drop (13), by (12), deparse1 (11), is.null (10), mean (10), nzchar (10), all (9), apply (9), debug (9), lapply (9), parent.frame (9), as.integer (7), integer (7), rank (7), seq.int (7), sort (6), unlist (6), array (5), assign (5), colnames (5), dim (5), log (5), message (5), mode (5), nrow (5), try (5), vector (5), as.numeric (4), call (4), diff (4), eval (4), gsub (4), as.data.frame (3), cbind (3), I (3), identical (3), setdiff (3), sub (3), sum (3), any (2), aperm (2), expand.grid (2), levels (2), max (2), ncol (2), seq (2), structure (2), suppressWarnings (2), t (2), beta (1), class (1), det (1), diag (1), duplicated (1), get (1), gl (1), gregexec (1), ifelse (1), intersect (1), is.factor (1), is.finite (1), match (1), min (1), new.env (1), numeric (1), parse (1), paste (1), prod (1), range (1), regmatches (1), replace (1), replicate (1), row.names (1), sample (1), sample.int (1), signif (1), strsplit (1), substitute (1), typeof (1), union (1), warning (1), which.max (1), which.min (1), with (1)

dynamite

ifelse_ (84), paste_rows (41), get_responses (17), data_lines_default (10), get_predictors (10), onlyif (10), model_lines_default (9), warning_ (9), prepare_channel_default (8), formula_rhs (6), get_quoted (5), as.data.frame.dynamitefit (4), get_families (4), has_past (4), coef.dynamitefit (3), evaluate_specials (3), get_formulas (3), assign_deterministic (2), complete_lags (2), create_blocks (2), cs (2), default_priors (2), default_priors_categorical (2), deterministic_response (2), extract_lags (2), extract_nonlags (2), find_lags (2), formula_lhs (2), formula_past (2), formula_terms (2), full_model.matrix (2), full_model.matrix_predict (2), get_originals (2), get_terms (2), indenter_ (2), join_dynamiteformulas (2), lag_ (2), parse_global_lags (2), parse_lags (2), parse_new_lags (2), parse_singleton_lags (2), prepare_eval_envs (2), prepare_lagged_response (2), stop_ (2), which_deterministic (2), which_stochastic (2), abort_factor (1), abort_negative (1), abort_nonunit (1), add_dynamiteformula (1), as_data_frame_alpha (1), as_data_frame_beta (1), as_data_frame_corr_nu (1), as_data_frame_default (1), as_data_frame_delta (1), as_data_frame_lambda (1), as_data_frame_nu (1), as_data_frame_omega (1), as_data_frame_omega_alpha (1), as_data_frame_phi (1), as_data_frame_sigma (1), as_data_frame_sigma_nu (1), as_data_frame_tau (1), as_data_frame_tau_alpha (1), as_draws_df.dynamitefit (1), as_draws.dynamitefit (1), assign_initial_values (1), assign_lags (1), assign_lags_init (1), aux (1), check_ndraws (1), check_newdata (1), check_priors (1), clear_nonfixed (1), confint.dynamitefit (1), create_blocks.default (1), create_data (1), create_functions (1), create_generated_quantities (1), create_model (1), create_parameters (1), create_transformed_data (1), create_transformed_parameters (1), data_lines_bernoulli (1), data_lines_beta (1), data_lines_binomial (1), data_lines_categorical (1), data_lines_exponential (1), data_lines_gamma (1), data_lines_gaussian (1), data_lines_negbin (1), data_lines_poisson (1), drop_terms (1), drop_unused (1), dynamite (1), dynamitechannel (1), dynamitefamily (1), dynamiteformula (1), dynamiteformula_ (1), evaluate_deterministic (1), fill_time (1), fill_time_predict (1), fitted.dynamitefit (1), formula_specials (1), formula.dynamitefit (1), generate_random_intercept (1), generate_sim_call (1), get_code (1), get_code.dynamitefit (1), get_code.dynamiteformula (1), get_data (1), get_data.dynamitefit (1), get_data.dynamiteformula (1), get_priors (1), get_priors.dynamitefit (1), get_priors.dynamiteformula (1), get_special_term_indices (1), impute_newdata (1), increment_formula (1), initialize_deterministic (1), is_supported (1), is.dynamitefamily (1), is.dynamitefit (1), is.dynamiteformula (1), lags (1), lines_wrap (1), locf (1), mcmc_diagnostics (1), mcmc_diagnostics.dynamitefit (1), message_ (1), model_lines_bernoulli (1), model_lines_beta (1), model_lines_binomial (1), model_lines_categorical (1), model_lines_exponential (1), model_lines_gamma (1), model_lines_gaussian (1), model_lines_negbin (1), model_lines_poisson (1), ndraws.dynamitefit (1), nobs.dynamitefit (1), parameters_lines_bernoulli (1), parameters_lines_beta (1), parameters_lines_binomial (1), parameters_lines_categorical (1), parameters_lines_default (1), parameters_lines_exponential (1), parameters_lines_gamma (1), parameters_lines_gaussian (1), parameters_lines_negbin (1), parameters_lines_poisson (1), parse_data (1), parse_newdata (1), parse_past (1), parse_present_lags (1), plot_betas (1), plot_deltas (1), plot_nus (1), plot.dynamitefit (1), predict_dynamitefit (1), predict.dynamitefit (1), prepare_channel_bernoulli (1), prepare_channel_beta (1), prepare_channel_binomial (1), prepare_channel_categorical (1), prepare_channel_exponential (1), prepare_channel_gamma (1), prepare_channel_gaussian (1), prepare_channel_negbin (1), prepare_channel_poisson (1), prepare_common_priors (1), prepare_prior (1), prepare_splines (1), prepare_stan_input (1), values (1), verify_lag (1)

utils

data (79), capture.output (1), combn (1)

stats

formula (21), var (7), df (5), sd (4), D (3), model.matrix.lm (3), na.action (3), na.pass (3), offset (3), complete.cases (2), setNames (2), terms (2), sigma (1)

dplyr

bind_rows (12), filter (7), mutate (3), summarise (3), left_join (2), matches (1), n (1)

graphics

mtext (10), title (9), pairs (2)

rlang

caller_env (16)

checkmate

test_character (3), test_flag (3), test_string (3), test_int (2)

glue

glue (11)

cli

cli_abort (2), qty (2), cli_inform (1), cli_warn (1)

ggplot2

labs (3), position_dodge (1)

posterior

summarise_draws (2), as_draws (1), ndraws (1)

tidyr

expand_grid (2), full_seq (1), unnest (1)

methods

is (2), new (1)

rstan

extract (2), check_hmc_diagnostics (1)

data.table

setDT (1), setkeyv (1)

bayesplot

mcmc_combo (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 30 files) and
2 authors
1 vignette
6 internal data files
14 imported packages
36 exported functions (median 7 lines of code)
404 non-exported functions in R (median 9 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_vignettes	2	85.7
files_tests	11	91.7
loc_R	5956	96.5	TRUE
loc_vignettes	877	89.0
loc_tests	2413	95.3	TRUE
num_vignettes	1	64.8
data_size_total	2661885	98.5	TRUE
data_size_median	349085	96.0	TRUE
n_fns_r	440	96.6	TRUE
n_fns_r_exported	36	82.0
n_fns_r_not_exported	404	97.8	TRUE
n_fns_per_file_r	8	83.4
num_params_per_fn	2	11.9
loc_per_fn_r	9	24.3
loc_per_fn_r_exp	7	13.5
loc_per_fn_r_not_exp	9	27.1
rel_whitespace_R	4	76.1
rel_whitespace_vignettes	13	68.1
rel_whitespace_tests	9	86.4
doclines_per_fn_exp	37	45.3
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	696	96.9	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
2903919221	R-CMD-check	success	a8d932	295	2022-08-22
2903919220	test-coverage	success	a8d932	295	2022-08-22

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following note:

checking installed package size ... NOTE
installed size is 11.1Mb
sub-directories of 1Mb or more:
data 7.2Mb
doc 1.0Mb
R 2.5Mb

R CMD check generated the following check_fail:

rcmdcheck_reasonable_installed_size

Test coverage with covr

Package coverage: 97.82

Cyclocomplexity with cyclocomp

No functions have cyclocomplexity >= 15

Static code analyses with lintr

lintr found the following 6 potential issues:

message	number of times
Avoid library() and require() calls in packages	5
unexpected symbol	1

5. Other Checks

Details of other checks (click to open)

✖️ The following 10 function names are duplicated in other packages:

- aux from seewave
- get_code from norgeo, rmonad, xpose
- get_data from canvasXpress.data, cbsodataR, cimir, completejourney, CVXR, danstat, deckgl, ecb, finnishgrid, ggPMX, ggvis, hydroscoper, insight, jtools, mapbayr, metacoder, missCompare, optimall, qrmtools, r4googleads, radiant.data, radous, rbedrock, rchallenge, rsimsum, SWIM, swissparl, tidyLPA, tidySEM, trending, tsmp, ugatsdb, xpose
- get_priors from CausalQueries, insight
- lags from smooth, tis, TTR
- mcmc_diagnostics from bpr, rater, rnmamod
- obs from metacoder, observer
- plot_deltas from spruce
- random from CoOL, decisionSupport, distributions3, gam, gamlss, ggdmc, lidR, messydates, simr, sodium
- splines from rpatrec

Package Versions

package	version
pkgstats	0.1.1.20
pkgcheck	0.1.0.9
srr	0.0.1.178

Editor-in-Chief Instructions:

Processing may not proceed until the items marked with ✖️ have been resolved.

@santikka

@santikka Thank you for your help debugging and improving our check system. Sorry the checks took so long to appear here.

@adamhsparks Please ignore the failing checks. The duplicated names check will soon be downgraded from a fail to a note. The other check is brand new. @santikka Feel free to modify your repo to describe return values, but do so at your own leisure, and consider all checks as passing for now. Thanks for all the help!

@mpadge

@mpadge No worries, glad to help! I will add the missing return values soon.

@mpadge

@mpadge About the return values: the system does not seem to recognize function aliases. For example in the above, obs and aux are aliases of dynamiteformula and thus have the same return value implicitly. If I explicitly add the return tag to these aliases as well, the resulting documentation looks rather silly with regards to the output value:

Value
A dynamiteformula object.

A dynamiteformula object.

A dynamiteformula object.

@ropensci-review-bot

@ropensci-review-bot assign @noamross as editor

@noamross

Assigned! @noamross is now the editor

@LucyMcGowan

Thank you all for bearing with us and following up! @LucyMcGowan and @nicholasjclark, you seem to have indicated in your follow-ups, but to be unambiguous, do the updates resolve any outstanding issues for acceptance at gold level?

@noamross

Yes, I approve acceptance at the gold level. Thanks @noamross!

@LucyMcGowan

Thank you all for bearing with us and following up! @LucyMcGowan and @nicholasjclark, you seem to have indicated in your follow-ups, but to be unambiguous, do the updates resolve any outstanding issues for acceptance at gold level?

Yes I approve acceptance as well. Thanks very much

@ropensci-review-bot

@ropensci-review-bot approve dynamite

@santikka

Approved! Thanks @santikka for submitting and @nicholasjclark, @LucyMcGowan for your reviews! 😁

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dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data #554

Due date for @LucyMcGowan: 2022-10-20
Archive: TBD
Version accepted: TBD
Language: en

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Checks for dynamite (v0.0.1)

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:

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dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data #554

Description

Due date for @LucyMcGowan: 2022-10-20 Archive: TBD Version accepted: TBD Language: en

Pre-submission Inquiry

General Information

Badging

Technical checks

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Activity

ropensci-review-bot commented on Aug 18, 2022

ropensci-review-bot commented on Aug 18, 2022

ropensci-review-bot commented on Aug 18, 2022

mpadge commented on Aug 23, 2022

ropensci-review-bot commented on Aug 23, 2022

ropensci-review-bot commented on Aug 23, 2022

ropensci-review-bot commented on Aug 23, 2022

Checks for dynamite (v0.0.1)

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:

mpadge commented on Aug 23, 2022

santikka commented on Aug 23, 2022

santikka commented on Aug 24, 2022

adamhsparks commented on Aug 25, 2022

ropensci-review-bot commented on Aug 25, 2022

31 remaining items

noamross commented on Dec 9, 2022

LucyMcGowan commented on Dec 9, 2022

nicholasjclark commented on Dec 13, 2022

noamross commented on Dec 13, 2022

ropensci-review-bot commented on Dec 13, 2022

santikka commented on Dec 14, 2022

ropensci-review-bot commented on Dec 14, 2022

Metadata

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Type

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Due date for @LucyMcGowan: 2022-10-20
Archive: TBD
Version accepted: TBD
Language: en

1. rOpenSci Statistical Standards (`srr` package)

4. `goodpractice` and other checks

3b. `goodpractice` results

`R CMD check` with rcmdcheck