Software Citation Tools

A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (< https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.

Measure Climate Scenario Alignment of Corporate Loans

These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment targets based on climate scenarios. They implement in R the last step of the free software 'PACTA' (Paris Agreement Capital Transition Assessment; < https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals.

Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) . The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.

Standardized Climate Indices Such as SPI, SRI or SPEI

Functions for generating Standardized Climate Indices (SCI). Functions for generating Standardized Climate Indices (SCI). SCI is a transformation of (smoothed) climate (or environmental) time series that removes seasonality and forces the data to take values of the standard normal distribution. SCI was originally developed for precipitation. In this case it is known as the Standardized Precipitation Index (SPI).

Spatial Functions for Heterogeneity and Climate Variability

A comprehensive suite of spatial functions created to analyze and assess data heterogeneity and climate variability in spatial datasets. This package is specifically designed to address the challenges associated with characterizing and understanding complex spatial patterns in environmental and climate-related data.

Test the Homogeneity of Kappa Statistics

Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) Jun-mo Nam (2003) Jun-mo Nam (2005) Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) Allan Donner, Michael Eliasziw, Neil Klar (1996) .

Analyze biotic homogenization of landscapes

Tools for assessing exotic species' contributions to landscape homogeneity using average pairwise Jaccard similarity and an analytical approximation derived in Harris et al. (2011, "Occupancy is nine-tenths of the law," The American Naturalist). Also includes a randomization method for assessing sources of model error.

Climatic Indices for Agriculture

Collection of functions to compute agroclimatic indices useful to zoning areas based on climatic variables and to evaluate the importance of temperature and precipitation for individual crops, or in general for agricultural lands.

Manipulate Time Series of Climate Reconstructions

Methods to easily extract and manipulate climate reconstructions for ecological and anthropological analyses, as described in Leonardi et al. (2023) . The package includes datasets of palaeoclimate reconstructions, present observations, and future projections from multiple climate models.

Iberian Actuarial Climate Index Calculations

Calculates the Iberian Actuarial Climate Index and its components—including temperature, precipitation, wind power, and sea level data—to support climate change analysis and risk assessment. See "Zhou et al." (2023) for further details.