Estimating and analyzing auto regressive integrated moving average
    (ARIMA) models. The primary function in this package is arima(), which fits 
    an ARIMA model to univariate time series data using a random restart
    algorithm. This approach frequently leads to models that have model 
    likelihood greater than or equal to that of the likelihood obtained by 
    fitting the same model using the arima() function from the 'stats' package. 
    This package enables proper optimization of model likelihoods, which is a 
    necessary condition for performing likelihood ratio tests. This package 
    relies heavily on the source code of the arima() function of the 'stats' 
    package. For more information, please see Jesse Wheeler and Edward L. 
    Ionides (2025) <doi:10.1371/journal.pone.0333993>.

Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.

Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.

Client for the Archaeology Knowledge Graph of Chile (ArKG,
    <https://arkg.cl>), providing access to Chilean archaeological dates
    (radiocarbon and thermoluminescence) via its SPARQL endpoint powered
    by 'MillenniumDB'.

Tools for quantitative analysis related to archaeological and historical problems for irregularly spaced time indexed observations, toward evaluating linear dependence and homogeneity over time. Methods include effect sizes for measuring homogeneity, simulation from a truncated Poisson distribution for random right-censoring of count data, and least-squares spectral analysis by lowest frequency iteration for model fitting. Collins-Elliott (2026) <https://volweb.utk.edu/~scolli46/sce_aqysuppl2026.pdf>.