An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

Functions for fitting continuous-time Markov and hidden
    Markov multi-state models to longitudinal data.  Designed for
    processes observed at arbitrary times in continuous time (panel data)
    but some other observation schemes are supported. Both Markov
    transition rates and the hidden Markov output process can be modelled
    in terms of covariates, which may be constant or piecewise-constant
    in time.

Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.

Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.

A 'Shiny' application for converting 'Excel'-based
    Life Cycle Inventory (LCI) data into 'SimaPro' CSV
    (Comma-Separated Values) format for use in Life Cycle
    Assessment (LCA) modeling. Developed by the Mine Sustainability
    Modeling Group (MSMG) at Missouri University of Science and
    Technology under NSF (National Science Foundation) funding
    (Award No. 2219086). See Pizzol (2022)
    <https://github.com/massimopizzol/Simapro-CSV-converter>
    for the original 'Python' implementation that inspired this tool.

Model evaluation based on a modified version of the recursive feature elimination algorithm. This package is designed to determine the optimal model(s) by leveraging all available features.