R-universe search: "effect-sizes"

Showing 6 of total 6 results (show query)

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.

bootstrapping confidence-interval effect-sizes moderation regression standardization standardized-moderation

48.1 match 1 stars 5.62 score 46 scripts

jeksterslab

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-intervals monte-carlo regression-effect-sizes standardized-regression-coefficients

21.0 match 1 stars 4.27 score 22 scripts

jeksterslab

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-intervals nonparametric-bootstrap regression-effect-sizes standardized-regression-coefficients

20.6 match 1 stars 4.16 score 18 scripts

ohdsi

LocalControl:Nonparametric Methods for Generating High Quality Comparative Effectiveness Evidence

Implements novel nonparametric approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes. While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups. For further details, please see: Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl: An R Package for Comparative Safety and Effectiveness Research. Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.

Maintained by Christophe G. Lambert. Last updated 4 months ago.

cpp

7.4 match 1 stars 4.56 score 12 scripts

bioc

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.

immunooncology statisticalmethod bayesian regression genomewideassociation transcriptomics rnaseq methylationarray batcheffect multiplecomparison

4.7 match 5.19 score 97 scripts

rlobenchain

NU.Learning:Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic (2019) <doi:10.1016/j.yrtph.2019.104418>.

Maintained by Bob Obenchain. Last updated 1 years ago.

15.2 match 1.00 score 2 scripts