Title: Functions for Performing Local FDR Estimation when Null and
Authors@R: person("Kris", "Sankaran", , "kriss1@stanford.edu", role = c("aut", "cre"))
Description: The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Packaged: 2015-05-05 04:23:44 UTC; krissankaran

Title: Locally Sparse Estimator of Generalized Varying Coefficient
Model for Asynchronous Longitudinal Data
Description: Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data by kernel-weighted estimating equation.
Imports: fda, Matrix, psych, splines, stats

Title: Local Control: An R Package for Generating High Quality
Author: Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert
Maintainer: Christophe G. Lambert <cglambert@unm.edu>
Description: Implements novel nonparametric approaches to address
biases and confounding when comparing treatments or exposures in
observational studies of outcomes. While designed and appropriate for use
in studies involving medicine and the life sciences, the package can be
used in other situations involving outcomes with multiple confounders.
The package implements a family of methods for nonparametric bias
correction when comparing treatments in cross-sectional, case-control,
and survival analysis settings, including competing risks with censoring.
The approach extends to bias-corrected personalized predictions of
treatment outcome differences, and analysis of heterogeneity of treatment
Imports: Rcpp, gss, cluster, lattice, stats, graphics

Package: LocalControl
Type: Package
Title: Nonparametric Methods for Generating High Quality Comparative
Effectiveness Evidence
Version: 1.0.1
Date: 2017-06-02
Author: Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert
Maintainer: Christophe G. Lambert <cglambert@unm.edu>
Description: Implements novel nonparametric approaches to address
biases and confounding when comparing treatments or exposures in
observational studies of outcomes. While designed and appropriate for use
in studies involving medicine and the life sciences, the package can be
used in other situations involving outcomes with multiple confounders.
The package implements a family of methods for nonparametric bias
correction when comparing treatments in cross-sectional, case-control,
and survival analysis settings, including competing risks with censoring.
The approach extends to bias-corrected personalized predictions of
treatment outcome differences, and analysis of heterogeneity of treatment
effect-sizes across patient subgroups.
License: Apache License 2.0 | file LICENSE
SystemRequirements: C++11
LazyData: TRUE
LinkingTo: Rcpp
Imports: Rcpp, gss, cluster, lattice, stats, graphics
RoxygenNote: 6.0.1
NeedsCompilation: yes
Depends: R (>= 3.0.0)
Repository: CRAN
Packaged: 2017-06-02 22:39:08 UTC; lambert
Date/Publication: 2017-06-03 06:27:20 UTC