File: uroot-intro.Rnw

package info (click to toggle)
r-cran-uroot 2.1-3-1
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 1,992 kB
  • sloc: makefile: 5
file content (50 lines) | stat: -rwxr-xr-x 1,812 bytes parent folder | download | duplicates (4)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
\documentclass[a4paper,11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\selectlanguage{english}
\usepackage[colorlinks=true, citecolor=blue, 
  linkcolor=blue, urlcolor=blue,
  bookmarksopen=false, pdfpagemode=UseOutlines]{hyperref}
\usepackage{Sweave}
\renewcommand{\baselinestretch}{1.3}
\newcommand{\email}[1]{\href{mailto:#1}{\normalfont\texttt{#1}}}
\topmargin -1cm
\textheight 24cm
\textwidth 14.2cm
\oddsidemargin 0.5cm
\evensidemargin 0.0cm

\title{\textsf{R} Package \textsf{uroot}}
\author{Javier L\'opez-de-Lacalle \\ \email{javlacalle@yahoo.es}}
\date{January 2017}

\begin{document}

%\VignetteIndexEntry{uroot-intro}
%\VignetteKeyword{seasonality}
%\VignetteKeyword{time series}
%\VignetteKeyword{unit roots}

\maketitle
\thispagestyle{empty}

In order to reduce the size of the package and the time required for checking it, 
the following documentation is provided outside the sources of the package: 

\begin{itemize}
\item An introduction to seasonal unit root tests implemented in the package is given here 
(Section 6 is outdated):
\begin{center}
\href{https://www.jalobe.com/doc/uroot.pdf}{https://www.jalobe.com/doc/uroot.pdf}.
\end{center}
\item The GPU parallel implementation of the bootstrapped HEGY test statistics is 
described here:
\begin{center}
\href{https://www.jalobe.com/doc/uroot-gpu.pdf}{https://www.jalobe.com/doc/uroot-gpu.pdf}.
\end{center}
\item \href{https://www.jalobe.com/blog/testing-for-seasonal-stability-canova-and-hansen-test-statistic/}{%
This post} illustrates the usage of the package by applying the Canova and Hansen test statistics to the data set 
employed in the original paper and computing p-values based on the response surface regression approach.
\end{itemize}

\end{document}