Package: MAARTS 1.0.0

MAARTS: Merger and Acquisition Autoregressive Time-Series Models

Implements comprehensive Merger and Acquisition ('M&A') Autoregressive ('AR') time-series models with full statistical analysis capabilities. The package provides parameter estimation, forecasting with confidence intervals (80%, 90%, 95%, 99%), descriptive statistics, stationarity tests (Augmented Dickey-Fuller ('ADF'), Phillips-Perron, Kwiatkowski-Phillips-Schmidt-Shin ('KPSS'), Dickey-Fuller Generalized Least Squares ('DF-GLS')), autocorrelation analysis (Autocorrelation Function ('ACF'), Partial Autocorrelation Function ('PACF')), model diagnostics (Ljung-Box, Box-Pierce), accuracy measures (Mean Squared Error ('MSE'), Mean Absolute Error ('MAE'), Mean Absolute Scaled Error ('MASE'), Root Mean Squared Error ('RMSE'), Symmetric Mean Absolute Percentage Error ('SMAPE'), F-statistic), residual diagnostics (normality tests, heteroscedasticity tests), model stability analysis, impulse response, information criteria (Akaike Information Criterion ('AIC'), Bayesian Information Criterion ('BIC'), Hannan-Quinn Information Criterion ('HQIC')), structural break analysis, spectral analysis, and Monte Carlo simulation. Models are based on: Kumar, Mudassir, and Agiwal (2024) <https://ph02.tci-thaijo.org/index.php/thaistat/article/view/253436>, Kumar, Mudassir, and Srivastava (2025) <doi:10.1007/s44199-025-00104-3>, Kumar and Mudassir (2025) <doi:10.19139/soic-2310-5070-2029>.

Authors:Shikhar Tyagi [aut, cre], Mohd Mudassir [aut], Vrijesh Tripathi [aut]

MAARTS_1.0.0.tar.gz
MAARTS_1.0.0.zip(r-4.7-any)MAARTS_1.0.0.zip(r-4.6-any)MAARTS_1.0.0.zip(r-4.5-any)
MAARTS_1.0.0.tgz(r-4.6-any)MAARTS_1.0.0.tgz(r-4.5-any)
MAARTS_1.0.0.tar.gz(r-4.7-any)MAARTS_1.0.0.tar.gz(r-4.6-any)
MAARTS_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MAARTS/json (API)

# Install 'MAARTS' in R:

install.packages('MAARTS', repos = c('https://shikhartyagi.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

ma_sample_data - Sample M&A Time-Series Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 1 stars 274 downloads 23 exports 45 dependencies

Last updated from:bb4b2898f6. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	185
source / vignettes	OK	376
linux-release	OK	151
macos-release	OK	112
macos-oldrel	OK	97
windows-devel	OK	102
windows-release	OK	98
windows-oldrel	OK	99
wasm-release	OK	131

Exports:ma_accuracy ma_acf_pacf ma_acf_plot ma_ar_fit ma_ars_model ma_descriptive_stats ma_diagnostic_tests ma_forecast ma_forecast_plot ma_histogram_plot ma_impulse_response ma_model_comparison ma_monte_carlo_simulation ma_multiple_merger_ar ma_normality_tests ma_pacf_plot ma_qq_plot ma_residual_diagnostics ma_residual_plot ma_spectral_analysis ma_stability_analysis ma_stationarity_tests ma_structural_break

Dependencies:cli colorspace cpp11 curl farver forecast fracdiff generics ggplot2 glue gridExtra gtable isoband jsonlite labeling lattice lifecycle lmtest magrittr MASS moments nlme nnet nortest numDeriv quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo rlang S7 sandwich scales strucchange timeDate tseries TTR urca vctrs viridisLite withr xts zoo

MAARTS: A Comprehensive Guide to M&A AR Time-Series Analysis

Rendered fromMAARTS-vignette.Rmdusingknitr::rmarkdown

Last update: 2026-07-26
Started: 2026-07-26

Help page	Topics
Forecast Accuracy Measures for M&A Time-Series	ma_accuracy
ACF and PACF Analysis for M&A Time-Series	ma_acf_pacf
ACF Plot using ggplot2	ma_acf_plot
AR Model Estimation for M&A Time-Series	ma_ar_fit
Merger and Acquisition AR with Spline (M-ARS) Model	ma_ars_model
Descriptive Statistics for M&A Time-Series	ma_descriptive_stats
Diagnostic Tests for M&A AR Models	ma_diagnostic_tests
Forecasting for M&A AR Models	ma_forecast
Forecast Plot using ggplot2	ma_forecast_plot
Histogram Plot using ggplot2	ma_histogram_plot
Impulse Response Function for M&A AR Models	ma_impulse_response
Model Comparison for M&A AR Models	ma_model_comparison
Monte Carlo Simulation for M&A AR Models	ma_monte_carlo_simulation
Multiple Merger AR Model with Explanatory Series	ma_multiple_merger_ar
Normality Tests for M&A Time-Series	ma_normality_tests
PACF Plot using ggplot2	ma_pacf_plot
Normal Q-Q Plot using ggplot2	ma_qq_plot
Residual Diagnostics for M&A AR Models	ma_residual_diagnostics
Residual Plot using ggplot2	ma_residual_plot
Sample M&A Time-Series Data	ma_sample_data
Spectral Analysis for M&A Time-Series	ma_spectral_analysis
Stability Analysis for M&A AR Models	ma_stability_analysis
Stationarity Tests for M&A Time-Series	ma_stationarity_tests
Structural Break Analysis for M&A Time-Series	ma_structural_break
Plot method for ma_forecast	plot.ma_forecast
Print method for ma_accuracy	print.ma_accuracy
Print method for ma_acf_pacf	print.ma_acf_pacf
Print method for ma_ar_fit	print.ma_ar_fit
Print method for ma_ar_model	print.ma_ar_model
Print method for ma_descriptive_stats	print.ma_descriptive_stats
Print method for ma_diagnostic_tests	print.ma_diagnostic_tests
Print method for ma_forecast	print.ma_forecast
Print method for ma_impulse_response	print.ma_impulse_response
Print method for ma_model_comparison	print.ma_model_comparison
Print method for ma_monte_carlo_simulation	print.ma_monte_carlo_simulation
Print method for ma_residual_diagnostics	print.ma_residual_diagnostics
Print method for ma_spectral_analysis	print.ma_spectral_analysis
Print method for ma_stability_analysis	print.ma_stability_analysis
Print method for ma_stationarity_tests	print.ma_stationarity_tests
Print method for ma_structural_break	print.ma_structural_break

Package: MAARTS 1.0.0

MAARTS: Merger and Acquisition Autoregressive Time-Series Models

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Help Manual

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