Found 552 packages in 0.11 seconds
Phenotypic Index Measures for Oak Decline Severity
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121)
Diversity Index Calculation & Visualisation for Taxa and Location
Repurpose occurrence data for calculating diversity index values, creating visuals, and generating species composition matrices for a chosen taxon and location.
Transitive Index Numbers for Cross-Sections and Panel Data
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005
Wavelet-Based Index of Storm Activity
A powerful system for estimating an improved wavelet-based index of magnetic storm activity, storm activity preindex (from individual station) and SQ variations. It also serves as a flexible visualization tool.
Urban Centrality Index
Calculates the Urban Centrality Index (UCI) as in Pereira et al.,
(2013)
Jaccard Index for Population Structure Identification
Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.
Wrapper for the Social Progress Index Data
In 2015, The 17 United Nations' Sustainable Development Goals were adopted. 'spiR' is a wrapper of several open datasets published by the Social Progress Imperative (< https://www.socialprogress.org/>), including the Social Progress Index (a synthetic measure of human development across the world). 'spiR''s goal is to provide data to help policymakers and researchers prioritize actions that accelerate social progress across the world in the context of the Sustainable Development Goals. Please cite: Warin, Th. (2019) "spiR: An R Package for the Social Progress Index",
Regression for Rank-Indexed Compositional Data
Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.
Computation of Survey Weighted PC Based Composite Index
An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices.
For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986)
Methods for Partial Linear Single Index Model
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at