Rizki Ananda Fauziah, Ika Yuni Wulansari
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.

Small Area Estimation Non-Parametric Based Nadaraya-Watson
Kernel
Wicak Surya Hasani[aut, cre],
Azka Ubaidillah[aut]
Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

Stochastic Approximation Expectation Maximization (SAEM)
algorithm
Emmanuelle Comets, Audrey Lavenu, Marc Lavielle.
The SAEM package implements the Stochastic Approximation
EM algorithm for parameter estimation in (non)linear mixed
effects models. The SAEM algorithm: - computes the maximum
likelihood estimator of the population parameters, without any
approximation of the model (linearization, quadrature
approximation,...), using the Stochastic Approximation
Expectation Maximization (SAEM) algorithm, - provides standard
errors for the maximum likelihood estimator - estimates the
conditional modes, the conditional means and the conditional
standard deviations of the individual parameters, using the
Hastings-Metropolis algorithm. Several applications of SAEM in
agronomy, animal breeding and PKPD analysis have been published
by members of the Monolix group (http://software.monolix.org/).
global.R SaemixData.R SaemixModel.R SaemixRes.R SaemixObject.R
main.R zzz.R