Predictors can be converted to one or more numeric
    representations using a variety of methods. Effect encodings using
    simple generalized linear models <arXiv:1611.09477> or nonlinear
    models <arXiv:1604.06737> can be used.  There are also functions for
    dimension reduction and other approaches.

Provides a smooth mapping of multidimensional points into
    low-dimensional space defined by a self-organizing map. Designed to work
    with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
    <doi:10.12688/f1000research.21642.1>.

Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.

Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).

Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation,
    the model-based unit-level approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    the area-level model (see "Estimates of income for small places: An 
    application of James-Stein procedures to Census Data" by Fay and Herriot (1979) 
    <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance 
    estimation methods, log and arcsin transformation, spatial, robust and measurement 
    error models), as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel. For a detailed description of the package and the methods used
    see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" 
    by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette 
    "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".