# Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data

## Documentation for package ‘NU.Learning’ version 1.5

- DESCRIPTION file
- Code demos. Use demo() to run them.

## Help Pages

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