sfaR

Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) doi:10.1111/1477-9552.12422, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) doi:10.1007/s11123-009-0159-1, and applied in Dakpo et al. (2021) doi:10.1111/agec.12683. Several possibilities in terms of optimization algorithms are proposed.

Last 30 days

This package has been downloaded 645 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,959 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Feb 05, 2025 with 77 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.