Bayesian Cluster Validity Index

Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. ) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.

Design of Portfolio of Stocks to Track an Index

Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. .

Activity Index Calculation using Raw 'Accelerometry' Data

Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) . The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes.

Fit Probabilistic Index Models

Fit a probabilistic index model as described in Thas et al, 2012: . The interface to the modeling function has changed in this new version. The old version is still available at R-Forge.

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 ) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 ; Rao et al., 2002 ) and Minimum spanning tree (Hill, 2004 ) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.

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) . The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values closer to 0 indicate more polycentric patterns and values closer to 1 indicate a more monocentric urban form.

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.