Provides functions to read in and manipulate air quality model output from Models3-formatted files.  This format is used by the Community Multiscale Air Quality (CMAQ) model.

Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests.

A lightweight, dependency-free data engine for R that provides 
    a grammar for tabular and time-series manipulation. Built entirely on 
    Base R, 'm61r' offers a fluent, chainable API inspired by modern data 
    tools while prioritizing memory efficiency and speed. It includes 
    optimized versions of common data verbs such as filtering, mutation, 
    grouped aggregation, and approximate temporal joins, making it an 
    ideal choice for environments where external dependencies are 
    restricted or where performance in pure R is required.

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>.