An interface to the algorithms of 'Interpretable AI'
    <https://www.interpretable.ai> from the R programming language.
    'Interpretable AI' provides various modules, including 'Optimal Trees' for
    classification, regression, prescription and survival analysis, 'Optimal
    Imputation' for missing data imputation and outlier detection, and 'Optimal
    Feature Selection' for exact sparse regression. The 'iai' package is an
    open-source project. The 'Interpretable AI' software modules are proprietary
    products, but free academic and evaluation licenses are available.

Provides a monthly summary of Iowa liquor (class E) sales from January 2015
    to October 2020. See the package website for more information, 
    documentation and examples. Data source: 
    Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.

The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <arXiv:2301.00360>.

Implementation of some of the formulations for the thermodynamic
	and transport properties released by the International Association for
	the Properties of Water and Steam (IAPWS). More specifically, the
	releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97,
	R9-97, R10-06(2009), R11-07(2019), R12-08, R15-11, R16-17(2018),
	R17-20 and R18-21 at <http://iapws.org>.

An implementation of the International Association for the
    Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary
    Water Substance for General and Scientific Use and on the releases for viscosity,
    conductivity, surface tension and melting pressure.

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).