Processes data from The Social Networks and Fertility Survey,
    downloaded from <https://dataarchive.lissdata.nl>, including correcting 
    respondent errors and transforming network data into network objects to
    facilitate analyses and visualisation.

Original idea was presented in the reference paper. Varghese et al. (2020, 74(1):35-42) "Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery". Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base.

Contains functions to fetch data from various data sources.
    The user first creates a catalog of objects from a data source,
    then fetches data from the catalog.  The package provides an easy
    way to access data from many different types of sources.

Collect  your data on digital marketing campaigns from Google Analytics using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Collect  your data on digital marketing campaigns from Salesforce using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.