Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.

Provides the density, distribution, quantile and generation functions of some obscure probability 
    distributions, including the doubly non-central t, F, Beta, and Eta distributions; 
    the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of 
    non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
    the product of non-central chi-squares to powers; the product of doubly non-central
    F variables; the product of independent normals.

Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.

Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.

Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.