priorsense - Prior Diagnostics and Sensitivity Analysis

[n-kall]

Submitting Author Name: Noa Kallioinen
Submitting Author Github Handle: @n-kall
Other Package Authors Github handles: (comma separated, delete if none) @topipa, @paul-buerkner, @avehtari
Repository: https://github.com/n-kall/priorsense
Version submitted: 1.1.1.9000
Submission type: Stats
Badge grade: bronze/silver/gold (select one)
Editor: @emitanaka
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: priorsense
Title: Prior Diagnostics and Sensitivity Analysis
Version: 1.1.1.9000
Authors@R: c(person("Noa", "Kallioinen", email = "noa.kallioinen@aalto.fi", role = c("aut", "cre", "cph")),
	     person("Topi", "Paananen", role = c("aut")),
	     person("Paul-Christian", "Bürkner", role = c("aut")),
	     person("Aki", "Vehtari", role = c("aut")),
	     person("Frank", "Weber", role = c("ctb"))
	     )
Description: Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.2
Imports:
    checkmate (>= 2.3.1),
    ggdist (>= 3.3.2),
    ggh4x (>= 0.2.5),
    ggplot2 (>= 3.5.1),
    grDevices (>= 3.6.2),
    matrixStats (>= 1.3.0),
    posterior (>= 1.6.0),
    rlang (>= 1.1.4),
    stats,
    tibble (>= 3.2.1),
    utils
Suggests:
    bayesplot (>= 1.11.1),
    brms (>= 2.22.0),
    cmdstanr (>= 0.8.1),
    iwmm (>= 0.0.1),
    philentropy (>= 0.8.0),
    quarto (>= 1.4.4),
    R2jags (>= 0.8),
    rstan (>= 2.32.6),
    testthat (>= 3.0.0),
    transport (>= 0.15),
    vdiffr (>= 1.0.8)
Config/testthat/edition: 3
Depends:
    R (>= 3.6.0)
VignetteBuilder: quarto
Additional_repositories:
    https://topipa.r-universe.dev,
    https://stan-dev.r-universe.dev
URL: https://github.com/n-kall/priorsense, https://n-kall.github.io/priorsense/
BugReports: https://github.com/n-kall/priorsense/issues
Config/Needs/website: quarto

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#697

General Information

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

The target audience is users of Bayesian models who would like to check the prior (and likelihood) sensitivity of their model. This can demonstrate the robustness of the results, as well as how important the prior choice is.

• Paste your responses to our General Standard G1.1 here, 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.

Response:

The \pkg{priorsense} package provides functions for prior and likelihood sensitivity analysis of Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood and is the first implementation of the method described in Kallioinen et al. (2023).

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Response: 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)

• Compliance with a good number of standards beyond those identified as minimally necessary

• Have a demonstrated generality of usage beyond one single envisioned use case: While originally developed for Stan models, priorsense has been extended to work with brms models, JAGS models and arbitrary posterior draws.

• Demonstrating excellence in compliance with multiple standards from at least two broad sub-categories: Aiming for excellence in documentation (6.1.1 and 6.3.1) with examples and vignettes, and (6.3.5) Visualization and summarization output

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?
• Do you intend for this package to go on Bioconductor?

Response:
priorsense is already on CRAN

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.

[ropensci-review-bot]

🚀

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]

Checks for priorsense (v1.1.1.9000)

git hash: cbeed8fd

• ✔️ 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 81.8%.
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.

Package License: GPL (>= 3)

---

1. rOpenSci Statistical Standards (srr package)

This package is in the following category:

• Exploratory Data Analysis

✔️ All applicable standards [v0.2.0] have been documented in this package (104 complied with; 22 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	392
internal	priorsense	121
internal	graphics	3
imports	checkmate	86
imports	stats	80
imports	posterior	30
imports	ggplot2	27
imports	utils	16
imports	grDevices	6
imports	matrixStats	3
imports	tibble	2
imports	ggh4x	1
imports	ggdist	NA
imports	rlang	NA
suggests	transport	5
suggests	philentropy	3
suggests	brms	2
suggests	iwmm	2
suggests	bayesplot	NA
suggests	cmdstanr	NA
suggests	quarto	NA
suggests	R2jags	NA
suggests	rstan	NA
suggests	testthat	NA
suggests	vdiffr	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 (84), length (41), list (40), return (35), for (13), sum (12), log (10), data.frame (8), min (8), paste0 (8), unique (8), is.null (6), mean (6), sub (6), max (5), cumsum (4), order (4), seq_along (4), sqrt (4), vector (4), ceiling (3), diag (3), exp (3), labels (3), mode (3), q (3), round (3), scale (3), seq_len (3), switch (3), abs (2), args (2), as.data.frame (2), attr (2), factor (2), get (2), lapply (2), levels (2), rep (2), seq (2), setdiff (2), subset (2), which (2), as.array (1), as.character (1), as.factor (1), as.numeric (1), colnames (1), diff (1), dim (1), drop (1), eigen (1), if (1), ifelse (1), is.numeric (1), merge (1), numeric (1), plot (1), pmax (1), pmin (1), rbind (1), rev (1), rle (1), rowSums (1), sort (1), summary (1), t (1), tcrossprod (1), unname (1)

priorsense

create_priorsense_data (6), get_powerscaling_details (6), pareto_k_mean (6), pareto_k_var (6), measure_divergence (5), cjs_dist (3), ess_mean (3), ess_var (3), hellinger_dist (3), js_dist (3), js_div (3), kl_dist (3), kl_div (3), ks_dist (3), powerscale_sequence (3), remove_unwanted_vars (3), whiten_draws (3), cdf_fun (2), create_priorsense_data.CmdStanFit (2), create_priorsense_data.stanfit (2), powerscale_examples (2), powerscale_gradients (2), prepare_plot (2), prepare_plot_data (2), quantile_weighted (2), above_one_comparison (1), below_one_comparison (1), compute_group (1), create_priorsense_data.default (1), create_priorsense_data.draws (1), create_priorsense_data.rjags (1), default_priorsense_colors (1), ewcdf (1), example_powerscale_model (1), find_alpha_threshold (1), find_alpha_threshold.default (1), find_alpha_threshold.priorsense_data (1), get_draws_CmdStanFit (1), get_draws_stanfit (1), is_constant (1), log_lik_draws (1), log_lik_draws.CmdStanFit (1), log_lik_draws.draws (1), log_lik_draws.stanfit (1), log_prior_draws (1), log_prior_draws.CmdStanFit (1), log_prior_draws.draws (1), log_prior_draws.stanfit (1), mad_weighted (1), mean_weighted (1), median_weighted (1), mv_kl_div (1), mv_wasserstein_dist (1), plot.priorsense_plot (1), powerscale (1), powerscale_derivative (1), powerscale_divergence_gradients (1), powerscale_gradients.default (1), powerscale_gradients.priorsense_data (1), powerscale_log_ratio_fun (1), powerscale_plot_dens (1), powerscale.default (1), powerscale.priorsense_data (1), rowsums_draws (1), scaled_log_ratio (1)

checkmate

assert_numeric (47), assertCharacter (7), assert_number (6), assert_vector (6), assert_logical (5), assertNumber (4), assert_character (2), assert_choice (2), assertChoice (2), assertNumeric (2), assertClass (1), assertFunction (1), assertLogical (1)

stats

weights (37), dist (8), density (6), loadings (6), sd (6), var (6), mad (2), median (2), approxfun (1), cov2cor (1), ecdf (1), ks.test (1), reshape (1), sigma (1), terms (1)

posterior

summarise_draws (9), subset_draws (6), pareto_khat (2), weight_draws (2), as_draws_array (1), as_draws_df (1), as_draws_matrix (1), bind_draws (1), merge_chains (1), mutate_variables (1), nchains (1), ndraws (1), quantile2 (1), resample_draws (1), variables (1)

ggplot2

aes (7), vars (6), element_blank (3), after_stat (2), ggplot (2), labeller (2), theme (2), ggproto (1), guide_legend (1), stat_ecdf (1)

utils

data (13), head (1), tail (1), vi (1)

grDevices

colors (5), colours (1)

transport

subwasserstein (2), wpp (2), wasserstein1d (1)

graphics

title (2), points (1)

matrixStats

weightedMad (1), weightedMean (1), weightedMedian (1)

philentropy

hellinger (1), jensen_shannon (1), kullback_leibler_distance (1)

brms

brmsterms (2)

iwmm

constrain_draws (1), moment_match (1)

tibble

as_tibble_col (2)

ggh4x

facetted_pos_scales (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 29 files) and
• 4 authors
• no vignette
• no internal data file
• 11 imported packages
• 37 exported functions (median 9 lines of code)
• 174 non-exported functions in R (median 13 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	29	88.3
files_inst	1	94.9
files_vignettes	0	0.0	TRUE
files_tests	15	92.2
loc_R	3050	89.1
loc_inst	28	23.8
loc_tests	770	78.9
num_vignettes	0	0.0	TRUE
n_fns_r	211	89.4
n_fns_r_exported	37	82.0
n_fns_r_not_exported	174	90.7
n_fns_per_file_r	4	63.6
num_params_per_fn	4	51.1
loc_per_fn_r	12	36.5
loc_per_fn_r_exp	9	19.7
loc_per_fn_r_not_exp	13	43.0
rel_whitespace_R	22	91.4
rel_whitespace_inst	29	29.1
rel_whitespace_tests	20	77.5
doclines_per_fn_exp	93	88.8
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	106	79.3

---

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
14618185403	pages build and deployment	success	979a6f	28	2025-04-23
14467436006	pkgcheck	failure	dbe734	1	2025-04-15
14617964150	pkgdown	success	cbeed8	103	2025-04-23
14617964116	R-CMD-check	success	cbeed8	513	2025-04-23

---

3b. goodpractice results

R CMD check with rcmdcheck

rcmdcheck found no errors, warnings, or notes

Test coverage with covr

Package coverage: 81.83

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
powerscale_sequence.priorsense_data	28
powerscale.priorsense_data	22
powerscale_plot_ecdf.powerscaled_sequence	19
powerscale_plot_dens.powerscaled_sequence	18
powerscale_gradients.priorsense_data	17
prepare_plot_data	16

Static code analyses with lintr

lintr found the following 82 potential issues:

message	number of times
Avoid library() and require() calls in packages	13
Lines should not be more than 80 characters. This line is 103 characters.	1
Lines should not be more than 80 characters. This line is 109 characters.	3
Lines should not be more than 80 characters. This line is 110 characters.	9
Lines should not be more than 80 characters. This line is 118 characters.	2
Lines should not be more than 80 characters. This line is 130 characters.	1
Lines should not be more than 80 characters. This line is 132 characters.	1
Lines should not be more than 80 characters. This line is 146 characters.	1
Lines should not be more than 80 characters. This line is 147 characters.	1
Lines should not be more than 80 characters. This line is 152 characters.	1
Lines should not be more than 80 characters. This line is 165 characters.	1
Lines should not be more than 80 characters. This line is 81 characters.	1
Lines should not be more than 80 characters. This line is 82 characters.	4
Lines should not be more than 80 characters. This line is 83 characters.	2
Lines should not be more than 80 characters. This line is 85 characters.	3
Lines should not be more than 80 characters. This line is 86 characters.	3
Lines should not be more than 80 characters. This line is 88 characters.	1
Lines should not be more than 80 characters. This line is 90 characters.	11
Lines should not be more than 80 characters. This line is 91 characters.	2
Lines should not be more than 80 characters. This line is 94 characters.	1
Lines should not be more than 80 characters. This line is 95 characters.	6
Lines should not be more than 80 characters. This line is 96 characters.	3
Lines should not be more than 80 characters. This line is 99 characters.	11

---

Package Versions

package	version
pkgstats	0.2.0.54
pkgcheck	0.1.2.126
srr	0.1.4.4

---

Editor-in-Chief Instructions:

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

[mpadge]

@n-kall Thanks for this exciting submission. We're hoping to find a stats editor to handle it in the coming days. You'll see progress as soon as that has happened.

[mpadge]

@ropensci-review-bot assign @emitanaka as editor

[ropensci-review-bot]

Assigned! @emitanaka is now the editor

[mpadge]

@n-kall Please note that @emitanaka will act as editor for your submission, but she will be away until early June. You'll have to wait until then before things proceed further. Sorry for any inconvenience, and thanks in advance for bearing with us.