Imports: dplyr, generics, ggplot2, kSamples, MASS, rlang, stats, tibble
Suggests: knitr, lintr, purrr, rmarkdown, spelling, testthat, tidyr,
Packaged: 2020-12-09 22:41:13 UTC; skloppenborg

Imports: dplyr, generics, ggplot2, kSamples, MASS, purrr, rlang, stats,
Suggests: knitr, lintr, rmarkdown, spelling, testthat, tidyr, vdiffr

Authors@R: person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr", comment = c(ORCID = "0000-0002-4857-7736"))
Suggests: censReg, AER, knitr, rmarkdown, maxLik, bookdown
Description: Conditional moments test, as proposed by Newey (1985) <doi:10.2307/1911011 > and Tauchen (1985) <doi:10.1016/0304-4076(85)90149-6>, useful to detect specification violations for models estimated by maximum likelihood. Methods for probit and tobit models are provided.
Author: Yves Croissant [aut, cre] (<https://orcid.org/0000-0002-4857-7736>)

Suggests: censReg, AER, knitr, rmarkdown, maxLik, bookdown, tobit1

Title: The Complex Multivariate Gaussian Distribution
Description: Various utilities for the complex multivariate Gaussian distribution.

Description: Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.

Title: Complex multivariate Gaussian distribution
Description: various utilities for the complex multivariate Gaussian distribution

Title: A Package for Coincidence Analysis (CNA)
Author: Ambuehl, Mathias, Michael Baumgartner, Ruedi Epple, Alexis Kauffmann and Alrik Thiem
Description: Provides functions for performing Coincidence Analysis (CNA).

Title: Causal Modeling with Coincidence Analysis
Authors@R: c(person("Mathias", "Ambuehl", role = c("aut", "cre", "cph"),
person("Michael", "Baumgartner", role = c("aut", "cph"),
Description: Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes. While previous versions have only been capable of processing dichotomous variables, the current version generalizes CNA for multi-value and continuous variables whose values are interpreted as membership scores in fuzzy sets.
Author: Mathias Ambuehl [aut, cre, cph],