person("Ruben", "Fernandez-Casal", , "rubenfcasal@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-5785-3739"))
Multidimensional nonparametric spatio-temporal (geo)statistics.
S3 classes and methods for multidimensional: linear binning,
local polynomial kernel regression, density and variogram estimation.
Nonparametric methods for simultaneous inference on both spatial trend
and variogram functions (for spatial processes).
Nonparametric residual kriging (spatial prediction).
Ruben Fernandez-Casal [aut, cre]
(<https://orcid.org/0000-0002-5785-3739>)

Validate Instrumental Variables using NPS
c(person("Fabian", "Sellmann", email="fa.sllmnn@gmail.com", role = c("aut", "cre")), person("Bojan", "Nicolic", email="bojan@bnikolic.co.uk", role=c("ctb", "cph")), person("Jörn", "Grahl", email="grahl@wiso.uni-koeln.de", role="ths"))
An R implementation of the Necessary and Probably Sufficient (NPS) test for finding valid instrumental variables, as suggested by Amit Sharma (2016, Working Paper) <http://amitsharma.in/pubs/necessary_probably_sufficient_iv_test.pdf>. The NPS test, compares the likelihood that a given set of observational data of the three variables Z, X and Y is generated by a valid instrumental variable model (Z -> X -> Y) to the likelihood that the data is generated by an invalid IV model.
npsCommon.R npsInvalid.R npsMain.R npsNecessary.R npsValid.R
RNested.R
Fabian Sellmann [aut, cre],
Bojan Nicolic [ctb, cph],
Jörn Grahl [ths]

A generalization of the nonparametric seasonality tests of
Hewitt et al. (1971) and Rogerson (1996)
The core of package 'npst' estimates the distribution of
the test statistic via Monte-Carlo simulation.

Generalization of Hewitt's Seasonality Test
Package 'npst' generalizes Hewitt's (1971) test for
seasonality and Rogerson's (1996) extension based on
Monte-Carlo simulation.

Package 'npst' generalizes Hewitt's (1971) test for seasonality and
Rogerson's (1996) extension based on Monte-Carlo simulation.

Contains functions for non-parametric survival analysis of
exact and interval-censored observations.