Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].

Implements the Classification-based on
    Association Rules (CBA) algorithm for association rule classification.
    The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
    contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested.

It fits a univariate left, right, or interval censored linear regression model
    with autoregressive errors, considering the normal or the Student-t distribution for the 
    innovations. It provides estimates and standard errors of the parameters, predicts 
    future observations, and supports missing values on the dependent variable.
    References used for this package:
    Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with 
    autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics,
    45(4), 375-392 <doi:10.1002/cjs.11338>.
    Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence 
    diagnostics for censored regression models with autoregressive errors. Australian & New 
    Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>.
    Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored 
    autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 
    52(3), 804-828 <doi:10.1002/cjs.11804>.