Other packages > Find by keyword >

InterNL  

Time Series Intervention Model Using Non-Linear Function
View on CRAN: Click here


Download and install InterNL package within the R console
Install from CRAN:
install.packages("InterNL")

Install from Github:
library("remotes")
install_github("cran/InterNL")

Install by package version:
library("remotes")
install_version("InterNL", "0.1.0")



Attach the package and use:
library("InterNL")
Maintained by
Dr. Md Yeasin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-04-18
Latest Update: 2024-04-18
Description:
Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.
How to cite:
Dr. Md Yeasin (2024). InterNL: Time Series Intervention Model Using Non-Linear Function. R package version 0.1.0, https://cran.r-project.org/web/packages/InterNL. Accessed 07 Oct. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited InterNL R package
View InterNL citation profile
Other R packages that InterNL depends, imports, suggests or enhances
Complete documentation for InterNL
Functions, R codes and Examples using the InterNL R package
Full InterNL package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

plaqr  
Partially Linear Additive Quantile Regression
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive qua ...
Download / Learn more Package Citations See dependency  
PairedData  
Paired Data Analysis
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis test ...
Download / Learn more Package Citations See dependency  
splm  
Econometric Models for Spatial Panel Data
ML and GM estimation and diagnostic testing of econometric models for spatial panel data. ...
Download / Learn more Package Citations See dependency  
blandr  
Bland-Altman Method Comparison
Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM B ...
Download / Learn more Package Citations See dependency  
skewlmm  
Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) ty ...
Download / Learn more Package Citations See dependency  
ggTimeSeries  
Time Series Visualisations Using the Grammar of Graphics
Provides additional display mediums for time series visualisations. ...
Download / Learn more Package Citations See dependency  

28,905

R Packages

247,686

Dependencies

76,495

Author Associations

28,906

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA