Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data.  'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'.  References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.
c(
person("Frederick", "Solt", email = "frederick-solt@uiowa.edu", role = c("aut", "cre")),
person("Trustees of", "Columbia University", role = "cph"))
rstan (>= 2.18.1), rstantools (>= 2.0.0), beepr, dplyr,
forcats, janitor, purrr, tibble, tidyr
Frederick Solt [aut, cre],
Trustees of Columbia University [cph]

Nonparametric Regression and Bandwidth Selection for Spatial
Models
c(person("Bastian", "Schaefer",
email = "bastian.schaefer@uni-paderborn.de", role = c("aut", "cre")),
person("Sebastian", "Letmathe", role = "ctb"), person("Yuanhua", "Feng",
role = "ctb"))
Nonparametric smoothing techniques for
data on a lattice and functional time series. Smoothing is done via
kernel regression or local polynomial regression, a bandwidth selection
procedure based on an iterative plug-in algorithm is implemented. This
package allows for modeling a dependency structure of the error terms
of the nonparametric regression model.
Methods used in this paper are described in
Beran/Feng (2002) <doi:10.1198/106186002420>,
Mueller/Wang (1994) <doi:10.2307/2533197>,
Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>,
Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.
Bastian Schaefer [aut, cre],
Sebastian Letmathe [ctb],
Yuanhua Feng [ctb]

c(person("Bastian", "Schaefer",
email = "bastian.schaefer@uni-paderborn.de", role = c("aut", "cre")),
person("Sebastian", "Letmathe", role = "ctb"))
Bastian Schaefer [aut, cre],
Sebastian Letmathe [ctb]

c(
person("Bastian", "Schaefer", , "bastian.schaefer@uni-paderborn.de", role = c("aut", "cre")),
person("Sebastian", "Letmathe", role = "ctb"),
person("Yuanhua Feng", role = "ths")
)
Bastian Schaefer [aut, cre],
Sebastian Letmathe [ctb],
Yuanhua Feng [ths]
Nonparametric smoothing techniques for data on a lattice and
functional time series. Smoothing is done via kernel regression or
local polynomial regression, a bandwidth selection procedure based on
an iterative plug-in algorithm is implemented. This package allows for
modeling a dependency structure of the error terms of the
nonparametric regression model.  Methods used in this paper are
described in Feng/Schaefer (2021)
<https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021)
<https://ideas.repec.org/p/pdn/ciepap/143.html>.
doParallel, foreach, fracdiff, parallel, plotly, Rcpp, stats