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georefdatar:Geosciences Reference Datasets
Reference datasets commonly used in the geosciences. These include standard atomic weights of the elements, a periodic table, a list of minerals including their abbreviations and chemistry, geochemical data of reservoirs (primitive mantle, continental crust, mantle, basalts, etc.), decay constants and isotopic ratios frequently used in geochronology, color codes of the chronostratigraphic chart. In addition, the package provides functions for basic queries of atomic weights, the list of minerals, and chronostratigraphic chart colors. All datasets are fully referenced, and a BibTeX file containing the references is included.
Maintained by Gerald Schuberth-Hlavač. Last updated 7 months ago.
earth-sciencegeochemical-datageochemistrygeologygeosciencegeosciencesmineralsperiodic-tablereference-data
25.6 match 2 stars 3.60 score 3 scriptscont-limno
LAGOSNE:Interface to the Lake Multi-Scaled Geospatial and Temporal Database
Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions for accessing lake water quality and ecological context data for the US.
Maintained by Jemma Stachelek. Last updated 2 years ago.
ecologygeosciencelimnologywater-quality
10.0 match 15 stars 6.77 score 98 scriptsrspatial
raster:Geographic Data Analysis and Modeling
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.
Maintained by Robert J. Hijmans. Last updated 2 months ago.
1.5 match 164 stars 17.05 score 58k scripts 555 dependentsfanhansen
creditmodel:Toolkit for Credit Modeling, Analysis and Visualization
Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.
Maintained by Dongping Fan. Last updated 3 years ago.
0.5 match 4 stars 3.48 score 15 scriptsmicheledalponte
varSel:Sequential Forward Floating Selection using Jeffries-Matusita Distance
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Maintained by Michele Dalponte. Last updated 4 years ago.
0.5 match 1.00 score 4 scripts