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NU.Learning  

Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data
View on CRAN: Click here


Download and install NU.Learning package within the R console
Install from CRAN:
install.packages("NU.Learning")

Install from Github:
library("remotes")
install_github("cran/NU.Learning")

Install by package version:
library("remotes")
install_version("NU.Learning", "1.5")



Attach the package and use:
library("NU.Learning")
Maintained by
Bob Obenchain
[Scholar Profile | Author Map]
First Published: 2023-09-30
Latest Update: 2023-09-30
Description:
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) ; Obenchain, Young and Krstic (2019) .
How to cite:
Bob Obenchain (2023). NU.Learning: Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data. R package version 1.5, https://cran.r-project.org/web/packages/NU.Learning. Accessed 01 Apr. 2025.
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