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Index of Multiple Deprivation Data for the UK
Index of Multiple Deprivation for UK nations at various geographical levels. In England, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from < https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019>. In Wales, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from < https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019>. In Scotland, deprivation data is for Data Zones, Intermediate Zones, and Council Areas based on data from < https://simd.scot>. In Northern Ireland, deprivation data is for Super Output Areas and Local Government Districts based on data from < https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017>. The 'IMD' package also provides the composite UK index developed by < https://github.com/mysociety/composite_uk_imd>.
Spatial Dispersion Index (SDI) Family of Metrics for Spatial/Geographic Networks
Spatial Dispersion Index (SDI) is a generalized measurement index, or rather a family of indices to evaluate spatial dispersion of movements/flows in a network in a problem neutral way as described in: Gencer (2023)
Calculate the Dendritic Connectivity Index in River Networks
Calculate and analyze ecological connectivity across the watercourse of river networks using the Dendritic Connectivity Index.
Matrices for Repeat-Sales Price Indexes
Calculate the matrices in
Shiller (1991,
Investigating New Projection Pursuit Index Functions
Projection pursuit is used to find interesting low-dimensional
projections of high-dimensional data by optimizing an index over all
possible projections. The 'spinebil' package contains methods to evaluate
the performance of projection pursuit index functions using tour methods.
A paper describing the methods can be found at
Turn Vectors and Lists of Vectors into Indexed Structures
Package designed for working with vectors and lists of vectors, mainly for turning them into other indexed data structures.
Calculates the Density-Based Clustering Validation (DBCV) Index
A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the < https://github.com/pajaskowiak/clusterConfusion/blob/main/R/dbcv.R> 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847),
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.
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)