lpme: Nonparametric Estimation of Measurement Error Models

Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method. See Huang and Zhou (2017) <doi:10.1080/10485252.2017.1303060>, Zhou and Huang (2016) <doi:10.1214/16-EJS1210>, Huang and Zhou (2020) <doi:10.1214/20-EJS1688>, and Zhou and Huang (2019) <doi:10.1080/03610918.2017.1402044>.

Version: 1.1.2
Depends: R (≥ 3.0.2)
Imports: Rcpp (≥ 0.11.1), decon, flexmix, splines, locpol
LinkingTo: Rcpp, RcppArmadillo (≥ 0.4.300.0)
Published: 2021-07-13
Author: Haiming Zhou [aut, cre, cph], Xianzheng Huang [aut]
Maintainer: Haiming Zhou <zhouh at niu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: lpme citation info
CRAN checks: lpme results

Downloads:

Reference manual: lpme.pdf
Package source: lpme_1.1.2.tar.gz
Windows binaries: r-devel: lpme_1.1.2.zip, r-devel-UCRT: lpme_1.1.2.zip, r-release: lpme_1.1.2.zip, r-oldrel: lpme_1.1.2.zip
macOS binaries: r-release (arm64): lpme_1.1.2.tgz, r-release (x86_64): lpme_1.1.2.tgz, r-oldrel: lpme_1.1.2.tgz
Old sources: lpme archive

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