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Interpolation and Extrapolation with Beta Diversity for Three Dimensions of Biodiversity
As a sequel to 'iNEXT', the 'iNEXT.beta3D' package provides functions to compute
standardized taxonomic, phylogenetic, and functional diversity (3D) estimates
with a common sample size (for alpha and gamma diversity) or sample coverage
(for alpha, beta, gamma diversity as well as dissimilarity or turnover indices).
Hill numbers and their generalizations are used to quantify 3D and to make
multiplicative decomposition (gamma = alpha x beta). The package also features
size- and coverage-based rarefaction and extrapolation sampling curves to
facilitate rigorous comparison of beta diversity across datasets.
See Chao et al. (2023)
Interpolation and Extrapolation for Three Dimensions of Biodiversity
Biodiversity is a multifaceted concept covering different levels of organization from
genes to ecosystems. 'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D)
of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional
diversity (FD). This package provides functions to compute standardized 3D diversity estimates
with a common sample size or sample coverage. A unified framework based on Hill numbers
and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates
are in the same units of species/lineage equivalents and can be meaningfully compared.
The package features size- and coverage-based rarefaction and extrapolation sampling
curves to facilitate rigorous comparison of 3D diversity across individual assemblages.
Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021)
Exploratory Analysis of Genetic and Genomic Data
Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.
Wavelet ANN Model
The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017)
Data Sets for 'ArchaeoPhases' Vignettes
Provides the data sets used to build the 'ArchaeoPhases' vignettes. The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size.
Collection and Analysis of Otolith Shape Data
Studies otolith shape variation among fish populations.
Otoliths are calcified structures found in the inner ear of teleost fish and their shape has
been known to vary among several fish populations and stocks, making them very useful in taxonomy,
species identification and to study geographic variations. The package extends previously described
software used for otolith shape analysis by allowing the user to automatically extract closed
contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000)
Measuring Ecosystem Multi-Functionality and Its Decomposition
Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales.
MARS Based ANN Hybrid Model
Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.
Integrative Lasso with Penalty Factors
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
Comparison of Variance - Covariance Patterns
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019)