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>.

Provides estimation utilities for binary Emax dose-response models. Includes Expectation-Maximization based maximum likelihood estimation when the binary response is missing, as well as bias-reduced estimators including Jeffreys-penalized likelihood, Firth-score, and Cox-Snell corrections.The methodology is described in Zhang, Pradhan, and Zhao (2025) <doi:10.1177/09622802251403356> and Zhang, Pradhan, and Zhao (2026) <doi:10.1080/10543406.2026.2627387>.

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.

Analyzes and quantifies ecosystem multifunctionality with functions to calculate multifunctionality richness (MFric), multifunctionality divergence (MFdiv), and multifunctionality regularity (MFreg). These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) <doi:10.1111/2041-210X.12143> and Chao et al. (2024) <doi:10.1111/ele.14336>.

Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

Initially designed to distribute code for estimating the Gaussian
  graphical model with Lasso regularization, also known as the graphical lasso
  (glasso), using an Expectation-Maximization (EM) algorithm based on work by
  Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct,
  code for estimating means and covariances (or the precision matrix) under a
  multivariate normal (Gaussian) distribution is also available.