Package: LocalControlStrategy
Title: Local Control Strategy for Robust Analysis of Cross-Sectional
        Data
Version: 1.4
Date: 2022-11-09
Author: Bob Obenchain 
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: R (>= 3.5.0), 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 or e-Exposure
  variable. Treatment variables are binary {either 1 = "new" or 0 = "control"}, while
  Exposure variables vary continuously over a finite range. LC Strategy starts by
  CLUSTERING experimental units (individual patients, US Counties, etc.) on their
  X-confounder characteristics. Clusters represent exclusive and exhaustive BLOCKS of
  relatively well-matched units. The implicit statistical model for LC is thus simple
  one-way ANOVA. Within-Block measures of effect-size are Local Rank Correlations (LRCs)
  when Exposure is numeric with (many) 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 or Researcher-Society communications about Heterogeneous Outcomes.  
License: GPL-2
URL: https://www.R-project.org, http://localcontrolstatistics.org
NeedsCompilation: no
Packaged: 2022-11-07 22:50:11 UTC; bobo
Repository: CRAN
Date/Publication: 2022-11-08 10:30:02 UTC
Built: R 4.1.3; ; 2023-04-17 13:31:23 UTC; windows
