Package: lpc
Type: Package
Title: Lassoed principal components for testing significance of features
Version: 1.0
Date: 2008-06-05
Author: Daniela M Witten and Robert Tibshirani
Maintainer: Daniela M Witten <dwitten@stanford.edu>
Description: Implements the LPC method of Witten&Tibshirani(2008) for identification of significant genes in a microarray experiment.
License: GPL (>= 2)
Packaged: Mon Jun  9 17:13:57 2008; dwitten

Title: Boundary Adaptive Local Polynomial Conditional Density Estimator
Description: Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2022).
Imports: Rcpp (>= 0.12.8), ggplot2, purrr, MASS, mvtnorm, stats
Packaged: 2022-05-04 15:33:48 UTC; rajitachandak

Imports: Rcpp (>= 0.12.8), ggplot2, purrr, MASS, mvtnorm, combinat,
Packaged: 2022-08-11 14:39:31 UTC; rajitachandak

Title: Local Polynomial Density Estimation and Inference
Author: Matias D. Cattaneo, Michael Jansson, Xinwei Ma
Description: Without imposing stringent distributional assumptions or shape restrictions, nonparametric density estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density estimator based on local polynomial regression, documented in Cattaneo, Jansson and Ma (2017): lpdensity() to construct local polynomial based density (and derivatives) estimator; lpbwdensity() to perform data-driven bandwidth selection; and lpdensity.plot() for density plot with robust confidence interval.

Description: Without imposing stringent distributional assumptions or shape restrictions, nonparametric density estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density estimator based on local polynomial regression, documented in Cattaneo, Jansson and Ma (2017a): lpdensity() to construct local polynomial based density (and derivatives) estimator; lpbwdensity() to perform data-driven bandwidth selection; and lpdensity.plot() for density plot with robust confidence interval.