Authors@R: c(person(given = "Siegfried", family = "Köstlmeier", role = c("aut", "cre"), email = "siegfried.koestlmeier@gmail.com", comment = c(ORCID = "0000-0002-7221-6981")), person(given = "Seppo", family = "Pynnonen", role = c("aut"), email = "sjp@uwasa.fi"))
Packaged: 2019-02-13 16:15:22 UTC; Siegfried

Description: Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <doi:10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
Packaged: 2021-01-11 12:58:48 UTC; LocalAdmin

Packaged: 2022-02-22 14:38:50 UTC; LocalAdmin

Packaged: 2019-08-20 08:17:49 UTC; LocalAdmin

Title: Diagnostics for Fine and Gray Model
Description: Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.

Title: Extract Coordinate System Metadata
Description: Obtain coordinate system metadata from various data formats. There
are functions to extract a 'CRS' (coordinate reference system,
Petroleum Survey Group, <http://www.epsg.org/>), 'PROJ4' <https://proj.org/>,
purely for getting simple metadata from in-memory formats, please use other
Author: Michael Sumner [aut, cre] (<https://orcid.org/0000-0002-2471-7511>)

Title: Nonlinear Regression Parameters Estimation by 'CRS4HC' and
Authors@R: c(person("Josef", "Tvrdík", email="josef.tvrdik@osu.cz", role="aut"), person("Tomáš", "Goryl", email="tomas.goryl@gmail.com", role=c("trl","cre")))
Description: Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.