Package: LocalControlStrategy
Title: Local Control Strategy for Robust Analysis of Cross-Sectional
        Data
Version: 1.3.3
Date: 2019-09-01
Author: Bob Obenchain 
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: cluster, lattice
Description: Especially when cross-sectional data are observational, effects of treatment
  selection bias and confounding are revealed by using the Nonparametric and Unsupervised 
  "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is to
  estimate the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and a t-Treatment variable. This
  t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of
  Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on their
  pre-exposure X-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively
  well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA.
  The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure
  is numeric with more than two levels. Otherwise, Treatment choice is Nested within
  BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between within-cluster
  y-Outcome Means ["new" minus "control"]. 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 ...assuming that X-Covariates influence only
  Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome.
  Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to
  aid Doctor-Patient communications about Personalized Medicine.  
License: GPL-2
URL: https://www.R-project.org, http://localcontrolstatistics.org
NeedsCompilation: no
Packaged: 2019-08-29 18:24:27 UTC; bobo
Repository: CRAN
Date/Publication: 2019-08-29 23:10:09 UTC
Built: R 4.0.2; ; 2020-07-15 15:43:00 UTC; unix
