Description: The astro package provides a series of functions, tools and routines in everyday use within astronomy. Broadly speaking, one may group these functions into 7 main areas, namely: cosmology, FITS file manipulation, the Sersic function, plotting, data manipulation, statistics and general convenience functions and scripting tools.

Title: Astronomy: time and position functions, misc. utilities
Description: Miscellaneous astronomy functions, utilities, and data.

Title: Astronomy: Time and Position Functions, Misc. Utilities

Description: Astrochonologic testing, astronomical time scale development, time series analysis

Title: A Computational Tool for Astrochronology
Description: Routines for astrochronologic testing, astronomical time scale construction, and time series analysis. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.

Author: Stephen Meyers [aut,cre], Alberto Malinverno [ctb], Linda Hinnov [ctb], Vincent Moron [ctb]
Imports: multitaper, IDPmisc, fields, doParallel, parallel, iterators,

Author: Stephen Meyers [aut,cre], Alberto Malinverno [ctb], Linda Hinnov [ctb], Christian Zeeden [ctb], Vincent Moron [ctb]

Imports: doParallel, fields, foreach, IDPmisc, iterators, multitaper,

Author: Stephen Meyers [aut,cre], Alberto Malinverno [ctb], Linda Hinnov [ctb], Christian Zeeden [ctb], Huaran Liu [ctb], Vincent Moron [ctb]
Description: Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
Imports: multitaper, parallel, doParallel,foreach, iterators, IDPmisc,

Maintainer: "Eric D., Feigelson" <edf@astro.psu.edu>
Description: A collection of 19 datasets from contemporary astronomical research.  They are described the textbook `Modern Statistical Methods for Astronomy with R Applications' by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets).  These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis.