Compute Seasonality Index, Seasonalized and Deseaonalised the Time Series Data

The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies — for each season (e.g. month, quarter, week) — the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data.

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.

Plant Stress Response Index Calculator

Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach.

The tRNA Adaptation Index

Functions and example files to calculate the tRNA adaptation index, a measure of the level of co-adaptation between the set of tRNA genes and the codon usage bias of protein-coding genes in a given genome. The methodology is described in dos Reis, Wernisch and Savva (2003) , and dos Reis, Savva and Wernisch (2004) .

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) .

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.

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.

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.

Functional Gait Deviation Index

A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject’s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics .