Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.

Ecological Inference by Linear Programming under Homogeneity
c(person(given = "Jose M.",
family = "Pavía",
role = c("aut", "cre"),
email = "jose.m.pavia@uv.es",
comment = c(ORCID = "0000-0002-0129-726X")),
person(given = "Rafael",
family = "Romero",
role = "aut",
email = "rromero@eio.upv.es"))
Provides a bunch of algorithms based on linear programming for estimating RxC ecological contingency tables (vote transitions matrices) using exclusively aggregate results from voting units under the homogeneity hypothesis. References: Romero, Pavia, Martin and Romero (2020) <doi:10.1080/02664763.2020.1804842>. Pavia and Romero (2021) Improving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming.
Jose M. Pavía [aut, cre] (<https://orcid.org/0000-0002-0129-726X>),
Rafael Romero [aut]

c(person(given = "Jose M.",
family = "Pavia",
role = c("aut", "cre"),
email = "jose.m.pavia@uv.es",
comment = c(ORCID = "0000-0002-0129-726X")),
person(given = "Rafael",
family = "Romero",
role = "aut",
email = "rromero@eio.upv.es"))
Provides a bunch of algorithms based on linear programming for estimating,
under the homogeneity hypothesis, RxC ecological contingency tables
(or vote transition matrices) using exclusively aggregate
data (from voting units). References:
Romero, Pavía, Martín and Romero (2020) <doi:10.1080/02664763.2020.1804842>.
Pavía and Romero (2021a) <doi:10.31124/advance.14716638.v1>.
Pavía and Romero (2021b) Symmetry estimating R×C vote transfer matrices from aggregate data.
Jose M. Pavia [aut, cre] (<https://orcid.org/0000-0002-0129-726X>),
Rafael Romero [aut]