Package: kbal 0.1.5

Borna Bateni

kbal: Kernel Balancing

Provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. 'kbal' is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. <https://www.researchgate.net/publication/299013953_Kernel_Balancing_A_flexible_non-parametric_weighting_procedure_for_estimating_causal_effects>.

Authors:Chad Hazlett [aut, cph], Ciara Sterbenz [aut], Erin Hartman [ctb], Alex Kravetz [ctb], Borna Bateni [aut, cre]

kbal_0.1.5.tar.gz
kbal_0.1.5.zip(r-4.7-x86_64)kbal_0.1.5.zip(r-4.7-arm64)kbal_0.1.5.zip(r-4.6-x86_64)kbal_0.1.5.zip(r-4.6-arm64)kbal_0.1.5.zip(r-4.5-x86_64)
kbal_0.1.5.tgz(r-4.6-x86_64)kbal_0.1.5.tgz(r-4.6-arm64)kbal_0.1.5.tgz(r-4.5-x86_64)kbal_0.1.5.tgz(r-4.5-arm64)
kbal_0.1.5.tar.gz(r-4.7-arm64)kbal_0.1.5.tar.gz(r-4.7-x86_64)kbal_0.1.5.tar.gz(r-4.6-arm64)kbal_0.1.5.tar.gz(r-4.6-x86_64)
kbal_0.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
kbal/json (API)

# Install 'kbal' in R:

install.packages('kbal', repos = c('https://chadhazlett.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/chadhazlett/kbal/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

lalonde - Data from National Supported Work program and Panel Study in Income Dynamics

On CRAN:

cpp

5.51 score 12 stars 135 scripts 463 downloads 14 exports 27 dependencies

Last updated from:3cb38cbd19. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	120
linux-devel-x86_64	OK	139
source / vignettes	OK	320
linux-release-arm64	OK	111
linux-release-x86_64	OK	132
macos-release-arm64	OK	185
macos-release-x86_64	OK	300
macos-oldrel-arm64	OK	187
macos-oldrel-x86_64	OK	435
windows-devel-arm64	OK	339
windows-devel-x86_64	OK	160
windows-release-arm64	OK	358
windows-release-x86_64	OK	94
windows-oldrel-x86_64	OK	112
wasm-release	OK	102

Exports:b_maxvarK biasbound calc_linearized_var calc_ses_cat_var dimw drop_multicollin ebalance_custom getdist getw kbal kpop kpop_summary makeK one_hot

Dependencies:cli codetools dplyr foreach generics glmnet glue iterators lattice lifecycle magrittr Matrix pillar pkgconfig R6 Rcpp RcppEigen RcppParallel rlang RSpectra shape survival tibble tidyselect utf8 vctrs withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Maximum Variance of Gaussian Kernel Matrix	b_maxvarK
Worst-Case Bias Bound due to Incomplete Balance	biasbound
Linearized variance for a kpop-weighted mean	calc_linearized_var
Weighted proportions and SEs for a categorical outcome	calc_ses_cat_var
Difference in Means and Difference in Weighted Means	dimw
Drop Multicollinear Columns	drop_multicollin
Modified version of ebalance (originally from Jens Hainmueller)	ebalance_custom
L1 Distance	getdist
Find Weights using Entropy Balancing.	getw
Kernel Balancing	kbal
Kernel balancing for survey reweighting (kpop).	kpop
Summarise outcomes from a kpop-weighted analysis	kpop_summary
Data from National Supported Work program and Panel Study in Income Dynamics	lalonde
Build the Gaussian Kernel Matrix	makeK
One-Hot Encoding for Categorical Data	one_hot
Print method for kpop_summary objects	print.kpop_summary