From: Peter Solymos <solymos_at_ualberta.ca>

Date: Sun, 26 Aug 2012 23:22:12 -0600

Date: Sun, 26 Aug 2012 23:22:12 -0600

Dear UseRs!

We are pleased to announce the release of our new package 'PVAClone'.

The 'PVAClone' package implements Population Viability Analysis (PVA) methodology using data cloning. The data cloning algorithm by Lele et al. (2007, 2010) is employed to compute maximum likelihood estimates of the state-space model parameters and the corresponding standard errors, heavily capitalizing on JAGS, dclone and dcme packages (see Solymos 2010).

The main components of the package include estimation of univariate population growth models, model selection and extinction risk estimation:

- Model Estimation *

*- computes maximum likelihood Estimation of the univariate population*

growth models, both in the presence or absence of observation error;

*- population time series with missing observations are also accommodated;*

- population growth models: Gompertz, Ricker, theta-logistic and the generalized Beverton-Holt;
- models can also be fitted by fixing a subset of model parameters to a priori values of interest;
- observation error is incorporated via the general state-space modeling framework.

- Model Selection *

*- we implement Ponciano et. al.'s (2009) data cloned likelihood ratio*

algorithm (DCLR) to compute likelihood ratios for comparing hierarchical (random effect) models;

*- this feature allows comparison of any two nested or non-nested*

state-space models fitted using the Model Estimation procedure above

*- for instance one can compare the state-space Generalized*

Beverton-Holt model with a logistic model, even when observations are missing;

*- the underlying function is pva.llr can also be called repeatedly to*

compute profile likelihood of a parameter of interest. - Extinction Risk Estimation (under development) *

*- data cloning based frequentist prediction of latent variables in a*

general hierarchical model (Lele et al. 2010) is used to forecast future abundance time series;

*- a large number of future population trajectories are generated under*

the observed data and estimated model parameters;

*- these are then used to estimate various extinction metrics (see*

Nadeem and Lele 2012).

Feedback, bug reports and feature requests are welcome!

Khurram and Peter

-- Khurram Nadeem knadeem_at_math.ualberta.ca University of Alberta Péter Sólymos solymos_at_ualberta.ca University of Alberta References Nadeem, K. & Lele, S.R. 2012. Likelihood based population viability analysis in the presence of observation error. Oikos, early online Ponciano, J. M. et al. 2009. Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning. Ecology 90: 356--362. Lele, S. R. et al. 2007. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecol. Lett. 10: 551--563. Lele, S. R. et al. 2010. Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning. J. Am. Stat. Assoc. 105: 1617--1625. Solymos, P. (2010): dclone: Data Cloning in R. The R Journal, 2(2): 29--37. _______________________________________________ R-packages mailing list R-packages_at_r-project.org https://stat.ethz.ch/mailman/listinfo/r-packagesReceived on Tue 28 Aug 2012 - 01:13:42 EST

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