Package: vecmatch 1.4.0.9000

vecmatch: Generalized Propensity Score Estimation and Matching for Multiple Groups

Implements the Vector Matching algorithm to match multiple treatment groups based on previously estimated generalized propensity scores. The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) <doi:10.1214/17-STS612>.

Authors:Mateusz Kolek [aut, cre, cph]

vecmatch_1.4.0.9000.tar.gz
vecmatch_1.4.0.9000.zip(r-4.7-any)vecmatch_1.4.0.9000.zip(r-4.6-any)vecmatch_1.4.0.9000.zip(r-4.5-any)
vecmatch_1.4.0.9000.tgz(r-4.6-any)vecmatch_1.4.0.9000.tgz(r-4.5-any)
vecmatch_1.4.0.9000.tar.gz(r-4.7-any)vecmatch_1.4.0.9000.tar.gz(r-4.6-any)
vecmatch_1.4.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
vecmatch/json (API)

# Install 'vecmatch' in R:
install.packages('vecmatch', repos = c('https://polymerase3.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/polymerase3/vecmatch/issues

Pkgdown/docs site:https://polymerase3.github.io

Datasets:
  • cancer - Patients with Colorectal Cancer and Adenoma

On CRAN:

Conda:

5.18 score 3 stars 5 scripts 529 downloads 13 exports 91 dependencies

Last updated from:91fddbe000 (on main). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK222
source / vignettesOK615
linux-releaseOK214
macos-releaseOK133
macos-oldrelOK116
windows-develOK170
windows-releaseOK160
windows-oldrelOK165
wasm-releaseOK138

Exports:balqualcsregionestimate_gpsget_select_paramshspinemake_opt_argsmatch_gpsmosaicoptimize_gpsraincloudrun_selected_matchingselect_optvspine

Dependencies:abindbackportsbootbroomcarcarDatachkclicodetoolscolorspacecorrplotcowplotcpp11DerivdigestdoBydplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggppggpubrggrepelggsciggsignifgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4lmtestlubridatemagrittrMASSMatchingMatrixMatrixModelsmgcvminqamodelrnlmenloptrnnetnumDerivoptmatchpbkrtestpillarpkgconfigplyrpolynomproductplotsprogressrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrlemonrstatixS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDateurcautf8vctrsviridisLitewithrxtszoo

Matching Patients in the cancer Dataset with vecmatch
Practical Example of the Vector Matching Workflow | Step 1: Data Exploration and Initial Imbalance Assessment | Step 2: Estimation of Generalized Propensity Scores | Step 3: Calculating Common Support Region Borders | Step 4: k-Means Clustering and Matching | Step 5: Post-Matching Quality Assessment | References
Rendered fromvecmatch.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2024-12-23

Optimizing the Matching Process with a Random Search Algorithm
Practical Example: Optimizing the Matching Process | Step 2: Run the Optimizer | Step 3: Select Optimal Configurations | Step 4: Refit the Optimized Model
Rendered fromoptimizing-matching.Rmdusingknitr::rmarkdown

Last update: 2025-12-01
Started: 2025-06-17