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".

Package providing calculation of Earth Mover's Distance (EMD).

Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.

Produce maximum likelihood estimates of common accuracy statistics
 for multiple measurement methods when a gold standard is not available. An R 
 implementation of the expectation maximization algorithms described in Zhou et 
 al. (2011) <doi:10.1002/9780470906514> with additional functions for creating 
 simulated data and visualizing results. Supports binary, ordinal, and 
 continuous measurement methods.