Description: Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Author: Javier Lopez-de-Lacalle [aut, cph],

Description: This package performs basic functions to fit and predict
periodic autoregressive time series models. These models are
discussed in the book P.H. Franses (1996) "Periodicity and
Stochastic Trends in Economic Time Series", Oxford University
Press. Data set analyzed in that book is also provided. NOTE:
the package was orphaned during several years. It is now only
maintained, but no major enhancement are expected, and the

Datetimes and timestamps are invariably an imprecise notation, with any
partial representation implying some amount of uncertainty. To handle this,
'parttime' provides classes for embedding partial missingness as a central
part of its datetime classes. This central feature allows for more ergonomic
use of datetimes for challenging datetime computation, including
calculations of overlapping date ranges, imputations, and more thoughtful
handling of ambiguity that arises from uncertain time zones. This package was
developed first and foremost with pharmaceutical applications in mind, but
aims to be agnostic to application to accommodate general use cases just as
Imports: crayon, lubridate, methods, pillar, utils, vctrs (>= 0.2.0)
Suggests: dplyr, withr, testthat, knitr, rmarkdown

Package: party
Title: A Laboratory for Recursive Part(y)itioning
Date: $Date: 2005/06/23 12:31:26 $
Version: 0.2-1
Author: Torsten Hothorn, Kurt Hornik and Achim Zeileis
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>
Description: Unbiased recursive partitioning in a conditional
inference framework. This package implements a unified
framework for recursive partitioning which embeds tree-structured
regression models into a well defined theory of conditional inference
procedures. Stopping criteria based on multiple test procedures
are implemented. The methodology is applicable to all kinds of
regression problems, including nominal, ordinal, numeric, censored
as well as multivariate response variables and arbitrary measurement
scales of the covariates. Extensible functionality for visualizing
tree-structured regression models is available.
Depends: R (>= 2.0.1), survival, grid, modeltools, coin
Suggests: ipred
SaveImage: yes
License: GPL
Packaged: Fri Jun 24 15:26:13 2005; hothorn