Submission: pvEBayes

[YihaoTancn]

Submitting Author Name: Yihao Tan
Submitting Author Github Handle: @YihaoTancn
Other Package Authors Github handles: (comma separated, delete if none) @c7rishi, @rmj3197
Repository: https://github.com/YihaoTancn/pvEBayes
Version submitted: 0.2.2
Submission type: Stats
Badge grade: gold
Editor: @LucyMcGowan
Reviewers: @k-doering-NOAA, @collin-cademartori

Due date for @k-doering-NOAA: 2026-04-08

Due date for @collin-cademartori: 2026-04-08
Archive: TBD
Version accepted: TBD
Language: en

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

Package: pvEBayes
Title: Empirical Bayes Methods for Pharmacovigilance
Version: 0.2.2
Authors@R: c(
    person(
    "Yihao", "Tan", 
    email = "yihaotan@buffalo.edu", 
    role = c("aut", "cre"),
    comment = c(ORCID = "0009-0002-9196-8166")
    ),
    person(
    "Marianthi", "Markatou", 
    role = "aut",
    comment = c(ORCID = "0000-0002-1453-8229")
    ),
    person(
    "Saptarshi", "Chakraborty", 
    role = c("aut"),
    comment = c(ORCID = "0000-0002-3121-9174")
    ),
    person(given = "Raktim", family = "Mukhopadhyay", 
      role = c("aut"),
      comment = c(ORCID = "0009-0007-8972-6682"))
    )
Maintainer: Yihao Tan <yihaotan@buffalo.edu>
Description: A suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) <doi:10.1002/sim.70195>, <doi:10.48550/arXiv.2512.01057>; Koenker and Mizera (2014) <doi:10.1080/01621459.2013.869224>; Efron (2016) <doi:10.1093/biomet/asv068>.
License: GPL-3
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.2
Depends: 
    R (>= 3.6.0)
Imports: 
    data.table,
    ggdist,
    ggfittext,
    ggplot2,
    glue,
    graphics,
    magrittr,
    methods,
    stats,
    wacolors,
    Rcpp,
    splines,
    CVXR
LinkingTo: 
    Rcpp,
    RcppEigen
LazyData: true
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/YihaoTancn/pvEBayes, https://yihaotancn.github.io/pvEBayes/
BugReports: https://github.com/YihaoTancn/pvEBayes/issues
VignetteBuilder: knitr

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#746

General Information

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

Target audience of pvEBayes would be statisticians/biostatisticians, pharmacovigilance researchers/practitioners, and epidemiologists who work with spontaneous reporting system (SRS) data.

Scientific application of 'pvEBayes': SRS data mining including (1) identifying AE-drug pairs that have observed case count substantially larger than their expected null value (signal detection); (2) estimating the signal strength of AE-drug pairs (signal strength estimation).

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

There is no other package that directly provides the methods implemented in pvEBayes, which is a suite of empirical Bayes approaches for signal detection and signal strength estimation in spontaneous reporting systems (SRS) data mining. Currently, there are some existing R packages applying SRS data mining. These packages focus only on signal detection.

Methods implemented in pvEBayes include Gamma-Poisson Shrinker (GPS), Koenker–Mizera method (KM), Efron, K-gamma and general-gamma. pvEBayes package provides the first implementation of K-gamma and general-gamma. There are a few packages that provide functions that can, in theory, be used to provide implementations of our methods; however, substantial modifications and customizations are needed.

The Gamma-Poisson Shrinker (GPS) is also available from another R package: openEBGM. In pvEBayes, the GPS is implemented by our bi-level Expectation Conditional Maximization (ECM) algorithm, offering faster and more stable GPS model fitting.

The KM has a general nonparametric empirical Bayes implementation in the REBayes package. But REBayes relies on Mosek, a commercial convex optimization solver, which may limit accessibility due to licensing issues. The pvEBayes package provides an alternative fully open-source implementation of the KM method using CVXR.

The Efron method has a general nonparametric empirical Bayes implementation in the deconvolveR package; however, that implementation does not support an exposure or offset parameter in the Poisson model, which corresponds to the expected null value ‘E’, a crucial quantity in SRS data mining. In pvEBayes, the implementation of Efron method is adapted and modified from deconvolveR to support incorporation of the exposure/offset term ‘E’ to model the SRS table.

Please include hyperlinked references to all other relevant software.

Relevant software are openEBGM, REBayes, deconvolveR, CVXR, Mosek

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

Yes

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 believe pvEBayes provides strong evidence toward these gold-grade for the following reseans:

1. pvEBayescompliance with a good number of standards. Specifically, the package satisfies 84 of 128 tagged standards overall, including 29 of 60 Bayesian software standards and 55 of 68 general standards. These numbers suggest that the package goes well beyond a minimal level of compliance and addresses a substantial proportion of both category-specific and general software standards.

2. pvEBayes demonstrates strong compliance with multiple standards across several broad sub-categories. For example, documentation (G1.0–G1.4a), input validation and interface design (G2.0–G2.3b), and testing and diagnostic support (G5.8a–G5.8d and BS5.3–BS5.5). These suggest that the package goes beyond minimum compliance in multiple sub-categories.

3. pvEBayes is designed for use beyond a single envisioned application. Although the motivating examples in the package and accompanying paper illustrated with specific SRS data analyses, the package is implemented for general advent event (AE)-drug frequency table analyses from spontaneous reporting systems. It is not restricted to specific drug class, or AEs, or one particular dataset. Instead, it provides a general nonparametric empirical Bayes framework that supports multiple methods and can be used for both signal detection and signal strength estimation.

4. We believe pvEBayes demonstrates strong internal package design. In particular, the package is organized around a main function, pvEBayes(), which fits nonparametric empirical Bayes models for SRS data, while related tasks are handled through separate function for model tuning (pvEBayes_tune()), printing (print()), summarizing (summary()), and plotting results (plot()). This design promotes clarity and ease for SRS data analysis.

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.

The current version of pvEBayes passes pkgcheck() on my local Windows PC under R 4.5.0, and I therefore believe the package itself is ready for submission. The failure in the GitHub Actions pkgcheck workflow appears to be environment-specific rather than due to an issue in pvEBayes itself.

Specifically, pvEBayes depends on CVXR, which recently had a substantial update. The new version of CVXR introduced a dependency on clarabel, and that dependency now uses a version 4 Cargo lockfile requiring Rust 1.78.0 or newer for compilation.

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.

[ropensci-review-bot]

🚀

Editor check started

👋

[ropensci-review-bot]

Checks for pvEBayes (v0.2.2)

git hash: 7aafd486

• ✔️ Package is already on CRAN.
• ✔️ 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 94.6%.
• ✔️ All goodpractice linters passed.
• ✔️ This is a statistical package which complies with all applicable standards
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.
• 👀 Function names are duplicated in other packages
• 👀 Examples should not use \dontrun{} unless really necessary.

(Checks marked with 👀 may be optionally addressed.)

Package License: GPL-3

---

1. rOpenSci Statistical Standards (srr package)

This package is in the following category:

• Bayesian and Monte Carlo

✔️ Your code includes the following standard which are not actual standards: [BS4.2]

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	282
internal	pvEBayes	42
internal	utils	8
internal	grDevices	2
imports	magrittr	106
imports	stats	25
imports	ggplot2	9
imports	data.table	8
imports	CVXR	6
imports	glue	5
imports	graphics	2
imports	methods	1
imports	wacolors	1
imports	splines	1
imports	ggdist	NA
imports	ggfittext	NA
imports	Rcpp	NA
suggests	knitr	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 (26), vapply (22), numeric (15), length (13), log (12), c (11), sum (11), t (11), as.vector (10), I (10), lapply (8), which.min (8), crossprod (7), max (7), rowSums (7), Sys.time (7), exp (6), for (6), ncol (6), nrow (5), seq_along (5), tcrossprod (5), by (4), character (4), colnames (4), drop (4), matrix (4), rep (3), sqrt (3), data.frame (2), dim (2), mean (2), plot (2), prod (2), range (2), rownames (2), seq_len (2), tryCatch (2), abs (1), apply (1), as.numeric (1), cbind (1), ceiling (1), colSums (1), conditionMessage (1), diag (1), if (1), is.numeric (1), objects (1), paste (1), paste0 (1), scale (1), strsplit (1), substring (1), tolower (1), toupper (1), unique (1), unlist (1)

magrittr

%>% (106)

pvEBayes

hess_fun (5), grad_fun (4), loglik_1 (4), loglik_not_pena (4), AIC.pvEBayes (3), BIC.pvEBayes (3), hess_fun_non_penal (2), NBmix_s1_EM_g_Rcpp (2), rtruncnorm (2), calculate_tilde_e (1), estimate_null_expected_count (1), extract_all_fitted_models (1), eyeplot_pvEBayes (1), generate_contin_table (1), heatmap_pvEBayes (1), is.pvEBayes (1), is.pvEBayes_tuned (1), logLik.pvEBayes (1), post_draw_discrete_cpp (1), post_draw_gmix_cpp (1), posterior_draws (1), tuning_efron (1)

stats

quantile (5), start (3), AIC (2), BIC (2), df (2), dnbinom (2), logLik (2), sd (2), weights (2), nlminb (1), rnorm (1), var (1)

ggplot2

element_blank (3), element_text (1), geom_tile (1), guide_legend (1), guides (1), position_dodge (1), scale_fill_gradientn (1)

data.table

melt (6), first (2)

utils

data (8)

CVXR

Maximize (1), Problem (1), psolve (1), sum_entries (1), value (1), Variable (1)

glue

glue (5)

graphics

hist (2)

grDevices

colors (1), palette (1)

methods

new (1)

splines

ns (1)

wacolors

scale_color_wa_d (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

The statistical properties of this package are all within expected ranges.

Details of statistical properties (click to open)

The package has:

• code in C++ (16% in 3 files) and R (84% in 9 files)
• 4 authors
• 1 vignette
• 5 internal data files
• 13 imported packages
• 20 exported functions (median 30 lines of code)
• 24 non-exported functions in R (median 8 lines of code)
• 17 R functions (median 7 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	9	61.1
files_src	3	87.1
files_vignettes	1	67.7
files_tests	5	80.9
loc_R	1631	80.7
loc_src	312	35.7
loc_vignettes	149	38.8
loc_tests	327	64.5
num_vignettes	1	64.4
data_size_total	80807	77.8
data_size_median	1617	65.5
n_fns_r	44	76.9
n_fns_r_exported	20	69.6
n_fns_r_not_exported	24	81.5
n_fns_src	17	84.6
n_fns_per_file_r	11	92.2
n_fns_per_file_src	6	88.7
num_params_per_fn	3	38.2
loc_per_fn_r	14	50.9
loc_per_fn_r_exp	30	65.8
loc_per_fn_r_not_exp	8	64.0
loc_per_fn_src	7	75.5
doclines_per_fn_exp	30	41.4
doclines_per_fn_not_exp	8	89.4
fn_call_network_size	94	79.8

---

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
23210937708	pkgcheck	failure	7aafd4	35	2026-03-17
23210937677	pkgdown.yaml	success	7aafd4	47	2026-03-17
23210937707	R-CMD-check.yaml	success	7aafd4	51	2026-03-17
23210937765	test-coverage.yaml	success	7aafd4	50	2026-03-17

---

3b. goodpractice results

R CMD check with rcmdcheck

R CMD check generated the following check_fail:

1. cyclocomp

Test coverage with covr

Package coverage: 94.59

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
pvEBayes	86
eyeplot_pvEBayes	63
pvEBayes_tune	55
heatmap_pvEBayes	42
generate_contin_table	16

---

5. Other Checks

Details of other checks (click to open)

✖️ The following function name is duplicated in other packages:

• • posterior_draws from marginaleffects

---

Package Versions

package	version
pkgstats	0.2.2.15
pkgcheck	0.1.2.276
srr	0.1.4.45

---

Editor-in-Chief Instructions:

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

[emilyriederer]

Thank you @YihaoTancn for opening your final submission. Noting that this is related to #746 (mentioned above but saying here so they are linked)

@LucyMcGowan , I was premature before but now it seems that we are actually ready for you!

[emilyriederer]

@ropensci-review-bot assign @LucyMcGowan as editor

[ropensci-review-bot]

Assigned! @LucyMcGowan is now the editor

[LucyMcGowan]

@ropensci-review-bot check srr

[ropensci-review-bot]

'srr' standards compliance:

• Complied with: 84 / 128 = 65.6% (bayesian: 29 / 60; general: 55 / 68)
• Not complied with: 44 / 128 = 34.4% (bayesian: 31 / 60; general: 13 / 68)

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

[LucyMcGowan]

Editor checks:

• Documentation: The package has sufficient documentation available online (README, pkgdown docs) to allow for an assessment of functionality and scope without installing the package. In particular,
  • Is the case for the package well made?
  • [ x Is the reference index page clear (grouped by topic if necessary)?
  • Are vignettes readable, sufficiently detailed and not just perfunctory?
• Fit: The package meets criteria for fit and overlap.
• Installation instructions: Are installation instructions clear enough for human users?
• Tests: If the package has some interactivity / HTTP / plot production etc. are the tests using state-of-the-art tooling?
• Contributing information: Is the documentation for contribution clear enough e.g. tokens for tests, playgrounds?
• License: The package has a CRAN or OSI accepted license.
• Project management: Are the issue and PR trackers in a good shape, e.g. are there outstanding bugs, is it clear when feature requests are meant to be tackled?

---

Editor comments

The initial checks look good to me!

---

[LucyMcGowan]

@ropensci-review-bot seeking reviewers

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