Estimating population abundance for replicated counts data is a computationally intensive problem. N-mixture models are used extensively in ecology to estimate population sizes, and to ascertain under-detection rates. Here I will discuss my new R package: quickNmix, which implements asymptotic solutions to the N-mixture likelihood function. The asymptotic solutions admit faster computation of the likelihood function, and the addition of parallel computing to the package can further increase computing speeds.
Parker, M.R.P., Li, Y., Elliott, L.T., Ma, J. and Cowen, L.L.E. (2021), Under-reporting of COVID-19 in the Northern Health Authority region of British Columbia. Canadian Journal of Statistics, 49: 1018-1038.
Parker, M.R.P., Pattison, V. & Cowen, L.L.E. (2020). Estimating population abundance using counts from an auxiliary population. Environmental Ecological Statistics 27, 509–526.
Parker, M. R. P. (2020). N-mixture models with auxiliary populations and for large population abundances [Thesis].
Introduction Population abundance models are used extensively in ecological studies, and they provide methods of estimation when only site and time replicated counts are available. These models have many possible applications beyond ecology, and can be applied in the study of disease prevalence and detection rates. Yearly counts of reported cases of depression, stratified by Health Service Delivery Area (HSDA), will be used to estimate both the total cases and the case detection rate (CDR) of depression in the Vancouver Coastal Health Authority (VCHA) region from the year 2000 to 2014.