R Observatory: Metrics for PatientLevelPrediction version 6.7.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

PatientLevelPrediction

6.7.0Package record

Develop Clinical Prediction Models Using the Common Data Model

0packages depend

7.5Kdownloads / year

89.3%test coverage

13/13flavors OK

Overview

About

Maintained by Egill FridgeirssonCRAN page ↗GitHub ↗Website ↗Issues ↗

A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) doi:10.1093/jamia/ocy032.

Changes in 6.7.0

New features

Added simulatePlpBenchmarkData() to generate semi-synthetic benchmark data from covariate profiles or fitted PLP models.
Added outcome-limited split settings for large data sets where training should use a target number of outcome-positive rows (#396).
Added Broken Adaptive Ridge logistic regression via setBrokenAdaptiveRidge(), including automatic ridge initialization and out-of-fold penalty selection (#673).
Added lifecycle-style custom hyperparameter generators.
Persisted model names in the results data model for downstream viewers.

Bug fixes

Generated iterative hard thresholding CV predictions using fitted prior variances and restored intercept fitting.

Show all changes in 6.7.0Hide the rest of the changes in 6.7.0

Ensured Cyclops CV refits honor thread, seed, tolerance, and iteration settings (#672).
Validated simulation outcome covariates and added support for user-supplied PLP outcome models.
Fixed scikit-learn SVC JSON loading to restore probability prediction, preserve sparse-model predictions, support dense models, and retain all-zero columns in sparse matrices (#682).
Defaulted missing evaluation types in evaluatePlp().
Corrected imputation messages for the number of missing features.
Added the existing diagnostic_predictors.probast_id column to the results data model specification (#674).

Performance and maintenance

Reduced hyperparameter database serialization overhead.
Raised and tested minimum dependency versions and added an R 4.1.3 dependency floor check.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("PatientLevelPrediction", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("OHDSI/PatientLevelPrediction")

Development version from OHDSI/PatientLevelPrediction. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-patientlevelprediction

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-PatientLevelPrediction

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-PatientLevelPrediction

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 11.7 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-10-18
ERROR11 OK, 2 ERROR2026-10-09
OK13 OK2026-09-29
ERROR12 OK, 1 ERROR2026-09-04
OK13 OK2026-08-05

Show 10 earlier changesHide 10 earlier changes

NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK13 OK2026-05-24
ERROR12 OK, 1 ERROR2026-05-17
OK13 OK2026-05-15
ERROR12 OK, 1 ERROR2026-05-13
OK14 OK2026-04-06
ERROR13 OK, 1 ERROR2026-04-04
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 6.7.0

READMEYes · 760 wordsVignettesYes · 14 · dynamicNEWSYes · 67% structured · mentions 6.7.0Citation fileYes · cites a publicationWebsiteYes · pkgdown

Help pages: 139 (10 internal)includes a package overview page
Examples that run: 50%62 run in a plain check (7 of them under a condition) · 48 only when donttest examples are enabled (R CMD check --as-cran) · 12 never run · 1 hold no code
Exported functions with examples: 96%
Documented parameters: 95%
Return-value docs: 98%
Help pages citing references: 1%

Also includes 3 demo scripts (run with demo())

Vignettes (14)

Adding Custom Data Splitting HTML
Adding Custom Feature Engineering Functions HTML
Adding Custom Patient-Level Prediction Algorithms HTML
Adding Custom Sampling HTML
Automatically Build Multiple Patient-Level Predictive Models HTML
Benchmark Tasks HTML
Best Practices HTML
Building patient-level predictive models HTML
Clinical Models HTML
Constrained Predictors HTML

Show 4 moreHide 4 vignettes

How these are measured

Downloads

7.5K

CRAN downloads in the past year

Rank #4,839 · ~21/day · ~624/mo

Releases R versions

Daily download trend is not available in this view yet.

82430 days

1.7K90 days

7.5K1 year

Compare downloads with other packages →

Also on149 r2u2 autocran

How downloads are counted

Repository

ohdsi/patientlevelprediction ↗Organization: OHDSI

223Stars

97Forks

37Open issues

3Open PRs

32Releases

1,913Commits

32Commit identities

hades

1,913 commits · Last activity 2026-09-21 · +1.4% stars, 30d

Responsiveness

How these are sampled

PRs merged (all time): 88%
Median days to close, 50 most recently updated closed issues: 40
Median days to close, 50 most recently updated closed PRs: 0

Stars over time

2025-10-17 · 2082026-10-08 · 223

How these are measured

PracticesBeta

10 development-tooling and community-health practices detected across 7 families in the upstream repository

Checks run against github.com/ohdsi/patientlevelprediction on 2026-10-18.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows

Governance and community (1)

Issue templates

Docs source (3)

pkgdown

GitHub Pages site last deployed 2026-09-29 · The repository has a site deploy workflow

pkgdown site last built 2026-09-29

Coverage (1)

Coverage reporting

Show all practicesHide 3 practices

Reproducibility and dev environment (1)

Dockerfile

Lint, format, editor (1)

RStudio project

Git structural (1)

.gitattributes

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

38 external dependencies (excludes base and recommended)

Installing PatientLevelPrediction adds 60 packages (35 with compiled code, 42 MB of source tarballs). Heaviest: stringi 11 MB, duckdb 4.6 MB, RSQLite 4.2 MB.

Depends (1)

R >= 4.1.0

Imports (16)

Andromeda Cyclops DatabaseConnector digest dbplyr dplyr FeatureExtractionMatrixmemuse ParallelLogger pROC PRROC rlang SqlRender tidyrutils

LinkingTo (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (7)

Egill Fridgeirsson

Author, Maintainer since 6.4.0 (2025)

Author

Author

Author

Author

Author

Observational Health Data Science and Informatics

Copyright holder

Releases (6)

Pick two releases to compare their code metrics.

6.7.0Latest
record
2026-09-21 · current release · diff ↗ ·
added 3 exports
createOutcomeLimitedSplitSettings, setBrokenAdaptiveRidge, simulatePlpBenchmarkData
R
R 4.6.0 released · 2026-04-24
6.6.0
record
2026-03-09 · diff ↗ ·
added 5 exports
computeAuprc, createHyperparameterSettings, createSklearnIterativeImputer, createTuningMetric, setRidgeRegression
6.5.1
record
2025-10-15 · diff ↗
6.5.0
record
2025-07-25 · diff ↗
6.4.1
record
2025-04-20 · diff ↗
R
R 4.5.0 released · 2025-04-11
6.4.0
record
2025-02-11
R
R 4.4.0 released · 2024-04-24

Package metadata

First published: 2025-02-11
Total releases: 6 / 1 yrs
License: Apache License 2.0 OSI
Minimum R: ≥ 4.1.0
Bundled data: 3.8 KB / 1 file
Download size: 3.1 MB

Cite

Cite this package

Run in R for the authors' entry:

citation("PatientLevelPrediction")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1093/jamia/ocy032.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for PatientLevelPrediction version 6.7.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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