Construction of Selection Index

Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices. It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) .

Calculate AZTI’s Marine Biotic Index

Calculate AZTI’s Marine Biotic Index - AMBI. The included list of benthic fauna species according to their sensitivity to pollution. Matching species in sample data to the list allows the calculation of fractions of individuals in the different sensitivity categories and thereafter the AMBI index. The Shannon Diversity Index H' and the Danish benthic fauna quality index DKI (Dansk Kvalitetsindeks) can also be calculated, as well as the multivariate M-AMBI index. Borja, A., Franco, J. ,Pérez, V. (2000) "A marine biotic index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments" .

Soil Quality Index

The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) .

House Price Indexes

Compute house price indexes and series using a variety of different methods and models common through the real estate literature. Evaluate index 'goodness' based on accuracy, volatility and revision statistics. Background on basic model construction for repeat sales models can be found at: Case and Quigley (1991) < https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: Bourassa et al (2006) . The package author's working paper on the random forest approach to house price indexes can be found at: < http://www.github.com/andykrause/hpi_research>.

Access the EPA Walkability Index

Provides helpers for querying the EPA Walkability Index < https://www.epa.gov/smartgrowth/national-walkability-index-user-guide-and-methodology> and working with Census GEOID/FIPS identifiers < https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html>.

Predictive Information Index ('PII')

A simple implementation of the Predictive Information Index ('PII').

Income Polarization Index

Extremely fast and memory efficient computation of the DER (or PaF) income polarization index as proposed by Duclos J. Y., Esteban, J. and Ray D. (2004). "Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. . The index may be computed for a single or for a range of values of the alpha-parameter and bootstrapping is also available.

Actuarial Climate Index

Computes the Actuarial Climate Index (ACI) and its components (temperature, precipitation, drought, wind, sea level) from gridded climate data ('NetCDF') and tide gauge records. Implements the methodology described in Garrido, Milhaud & Olympio (2025) < https://hal.science/hal-04491982v2> for a French/European actuarial climate index, building on the American Academy of Actuaries framework, to any country in the world.

Genie: Fast and Robust Hierarchical Clustering

Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 ). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 ). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'.

Working with Index Numbers

A set of utilities for manipulating index numbers series including chain-linking, re-referencing, and computing growth rates.