UNRCINT

This package is expected to help students in undertaking Unit Root Testing. The package consist of two main functions:

• urootp Conducts unit root test on the entire data frame and generates the test statistic with it’s associated p-values

• urootc Conducts unit root test on the entire data frame and generates the test statistic with it’s associated critical values at 1%, 5% and 10% respectively.

The package generate unit root test results based on:

• The Augmented Dickey Fuller (ADF) Test

• The Phillips Perron (PP) Test and

• The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test

Installation

You can install the development version of UNRCINT from GitHub with:

remotes::install_github("wgcantah/UNRCINT")

Example

This is a basic example which shows you how to solve a common problem:

library(UNRCINT)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(data)
#>     organic          regular      
#>  Min.   : 82.28   Min.   : 66.35  
#>  1st Qu.:116.24   1st Qu.: 95.68  
#>  Median :164.79   Median :142.96  
#>  Mean   :163.81   Mean   :143.76  
#>  3rd Qu.:212.33   3rd Qu.:190.08  
#>  Max.   :266.70   Max.   :243.56

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.

You can also embed plots, for example:

#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> Warning in tseries::pp.test(var_data): p-value smaller than printed p-value
#> Warning in tseries::kpss.test(var_data): p-value smaller than printed p-value
#> Warning in tseries::kpss.test(var_data): p-value smaller than printed p-value
#>         Variable   ADF ADF.P     PP PP.P KPSS KPSS.P
#> organic  organic -2.26  0.47 -72.04 0.01  3.7   0.01
#> regular  regular -1.96  0.59  -7.31 0.70  3.7   0.01

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.