R-universe search: lichen

Showing 6 of total 6 results (show query)

plant-data

ritalic:Interface to the ITALIC Database of Lichen Biodiversity

A programmatic interface to the Web Service methods provided by ITALIC (<https://italic.units.it>). ITALIC is a database of lichen data in Italy and bordering European countries. 'ritalic' includes functions for retrieving information about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys. More information about the data is available at <https://italic.units.it/?procedure=base&t=59&c=60>. The API documentation is available at <https://italic.units.it/?procedure=api>.

Maintained by Matteo Conti. Last updated 16 days ago.

biodiversity ecology fungi italic lichen

36.1 match 2 stars 4.04 score 6 scripts

vegandevs

vegan:Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Maintained by Jari Oksanen. Last updated 18 days ago.

ecological-modelling ecology ordination fortran openblas

5.6 match 472 stars 19.41 score 15k scripts 440 dependents

jillbo1000

EZtune:Tunes AdaBoost, Elastic Net, Support Vector Machines, and Gradient Boosting Machines

Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune_cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

Maintained by Jill Lundell. Last updated 3 years ago.

10.2 match 4.76 score 38 scripts 1 dependents

kelvynbladen

randomForestVIP:Tune Random Forests Based on Variable Importance and Plot Results

Functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

Maintained by Kelvyn Bladen. Last updated 1 years ago.

4.8 match 3.78 score 12 scripts

jarioksa

twinspan:Two-Way Indicator Species Analysis

Classification of biological communities based on splitting first axis of Correspondence Analysis for the current subset of the data, and finding species that best indicate the splits. The method is particularly popular in vegetation science.

Maintained by Jari Oksanen. Last updated 4 months ago.

fortran

3.5 match 7 stars 4.10 score 18 scripts

cran

ADER:Data Analysis in Ecology

Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-833-3).

Maintained by Marcelino De la Cruz. Last updated 1 years ago.

4.0 match 1.00 score