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Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Latent Variable Models for Networks
Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.
Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)
Disaggregate Variable Costs
These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.
Indices for Single-Case Research
Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011)
Analysis of MEDITS-Like Survey Data
Set of functions working with survey data in the format of the MEDITS project < https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.