c(person("Pavel Pimenov", role=c("aut", "cre"), email="pimenov@knoema.com"),
person("Ekaterina Chirkova", role="aut", email="chirkova@knoema.com"),
person("Aleksey Filippov", role="aut", email="afilippov@knoema.com"),
person("Knoema", role="cph"))
Pavel Pimenov [aut, cre],
Ekaterina Chirkova [aut],
Aleksey Filippov [aut],
Knoema [cph]

'Knoema' has the largest collection of public data and statistics on the Internet featuring about 2.5 billion time series from thousands of sources. Using 'Knoema' API users can access the data in the 'Knoema' repository and use rich R calculations in order to analyze the data. Because data in 'Knoema' is time series data, 'Knoema' function offers data in a number of formats usable in R such as 'ts', 'xts' or 'zoo'. For more information about 'Knoema' API go to <https://knoema.com/dev/docs>.

Discriminating Well Surveyed Spatial Units from Exhaustive
Biodiversity Databases
Cástor Guisande González and Jorge M. Lobo
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.

It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
R (>= 3.0), fossil, plotrix, sp, splancs, vegan

R (>= 3.0), fossil, mgcv, plotrix, sp, vegan

Knowledge-Based Guided Regularized Random Forest
Original code developed by Xin Guan, modified and added functions developed by Li Liu
Random Forest (RF) and Regularized Random Forest can be used for feature selection. Moreover, by Guided Regularized Random Forest, statistical-based weights are used to guide the regularization of random forest and further used for feature selection. This package can integrate prior information from multiple domains (statistical based and knowledge domain) to guide the regularization of random forest and feature selection. For more details, see reference: Guan X., Liu L. (2018) <doi:10.1007/978-3-319-78759-6_1>.