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sfcheung
stdmod:Standardized Moderation Effect and Its Confidence Interval
Functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Also includes simple-to-use functions for computing conditional effects (unstandardized or standardized) and plotting moderation effects.
Maintained by Shu Fai Cheung. Last updated 6 months ago.
bootstrappingconfidence-intervaleffect-sizesmoderationregressionstandardizationstandardized-moderation
48.1 match 1 stars 5.62 score 46 scriptsjeksterslab
betaMC:Monte Carlo for Regression Effect Sizes
Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). 'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2023 <doi:10.3758/s13428-023-02114-4>) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>) to generate confidence intervals effect sizes in regression.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 2 months ago.
confidence-intervalsmonte-carloregression-effect-sizesstandardized-regression-coefficients
21.0 match 1 stars 4.27 score 22 scriptsjeksterslab
betaNB:Bootstrap for Regression Effect Sizes
Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 2 months ago.
confidence-intervalsnonparametric-bootstrapregression-effect-sizesstandardized-regression-coefficients
20.6 match 1 stars 4.16 score 18 scriptsbioc
bacon:Controlling bias and inflation in association studies using the empirical null distribution
Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores.
Maintained by Maarten van Iterson. Last updated 5 months ago.
immunooncologystatisticalmethodbayesianregressiongenomewideassociationtranscriptomicsrnaseqmethylationarraybatcheffectmultiplecomparison
4.7 match 5.19 score 97 scripts