Title: Validate Instrumental Variables using NPS
Authors@R: 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"))
Description: 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.
Collate: npsCommon.R npsInvalid.R npsMain.R npsNecessary.R npsValid.R

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

Title: Generalization of Hewitt's Seasonality Test
Description: Package 'npst' generalizes Hewitt's (1971) test for
seasonality and Rogerson's (1996) extension based on

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

Description: Contains functions for non-parametric survival analysis of

X-CRAN-Comment: Orphaned on 2020-05-06 as requires 'lsei'
which is orphaned and by the same non-maintainer.

Description: Non-parametric survival analysis of exact and
interval-censored observations. The methods implemented

Title: Sample Size and Power Calculation for Common Non-Parametric
Authors@R: c(person("Godwin", "Yung", email="godwin.y.yung@gmail.com", role=c("aut", "cre")),
Description: A number of statistical tests have been proposed to compare two
survival curves, including the difference in (or ratio of) t-year
survival, difference in (or ratio of) p-th percentile survival, difference in
(or ratio of) restricted mean survival time, and the weighted log-rank test.
Despite the multitude of options, the convention in survival studies is to assume
proportional hazards and to use the unweighted log-rank test for design and
analysis. This package provides sample size and power
calculation for all of the above statistical tests with allowance for
flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential,
mixture cure). It is the companion R package to the paper by Yung and Liu (2019)
<doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may
specify which approximations they wish to use to estimate the large-sample mean
and variance. The default option has been shown to provide substantial
improvement over the conventional sample size and power equations based on Schoenfeld
Suggests: knitr, rmarkdown, tidyverse, ggplot2