Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data

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Documentation for package ‘NU.Learning’ version 1.5

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NU.Learning-package NU.Learning: Nonparametric and Unsupervised Adjustment for Bias and Confounding
confirm Confirm that Clustering in Covariate X-space yields an "adjusted" LTD/LRC effect-size Distribution
ivadj Instrumental Variable LAO Fitting and Smoothing
KSperm Simulate a p-value for the significance of the Kolmogorov-Smirnov D-statistic from confirm().
lrcagg Calculate the observed Distribution of LRCs in NU.Learning
ltdagg Calculate the Observed Distribution of LTDs in NU.Learning
mlme Create a <<Most-Like-Me>> data.frame for a specified X-Confounder vector: xvec
mlme.stats Print Summary Statistics for One or More "Most-Like-Me" Histogram Pairs.
NUcluster Hierarchical Clustering of experimental units (such as patients) in X-covariate Space
NUcompare Display NU Sensitivity Graphic for help in choice of K = Number of Clusters
NUsetup Specify KEY parameters used in NU.Learning to "design" analyses of Observational Data.
pci15k Six-month Survival, Cardiac cost and Baseline Covariate data for 15,487 PCI patients.
plot.ivadj Display an Instrumental Variable (LAO) plot with Linear and smooth.spline Fits
plot.lrcagg Display Visualizations of an Observed LRC Distribution in NU.Learning
plot.ltdagg Display Visualizations of an Observed LTD Distribution in NU.Learning
plot.mlme Display a Pair (or Pairs) of Histograms showing LOCAL effect-sizes for Patients "Most-Like-Me".
pmdata Particulate Matter, Mortality and Other data for 2980 US Counties
print.mlme Print Summary Statistics on Local effect-size Estimates for Patients "Most-Like-Me".
radon Radon exposure and lung cancer mortality data for 2,881 US counties in 46 States.
reveal.data Create a data.frame for use in Prediction of a LTD/LRC effect-size Distribution