Robust Selection Algorithm

An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) < http://proceedings.mlr.press/v108/cisneros20a.html>.

Penalized Partial Least Squares

Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing. The method is described and applied to simulated and experimental data in Kraemer et al. (2008) .

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) for more details.

Four-Step Biodiversity Analysis Based on 'iNEXT'

Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) for statistical background.

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) , choose from conducting either a Fourier or wavelet see Gençay et al (2001) transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects.

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.

Species-Richness Prediction and Diversity Estimation with R

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.