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Datasets to Measure the Alignment of Corporate Loan Books with Climate Goals
These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (< https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R.
PCIC Implementation of Climdex Routines
PCIC's implementation of Climdex routines for computation of extreme climate indices. Further details on the extreme climate indices can be found at < http://etccdi.pacificclimate.org/list_27_indices.shtml> and in the package manual.
Tools to Match Corporate Lending Portfolios with Climate Data
These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (< https://www.transitionmonitor.com/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.
Pipe-Friendly Framework for Basic Statistical Tests
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
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.
Statistical Tools for Modelling Climate-Health Impacts
Tools for producing climate-health indicators and supporting
official statistics from health and climate data. Implements analytical
workflows for temperature-related mortality, wildfire smoke exposure,
air pollution, suicides related to extreme heat, malaria, and
diarrhoeal disease outcomes, with utilities for descriptive statistics, model
validation, attributable fraction and attributable number estimation,
relative risk estimation, minimum mortality temperature estimation,
and plotting for reporting. These six indicators are endorsed by
the United Nations Statistical Commission for inclusion in the
Global Set of Environment and Climate Change Statistics.
Implemented methods include distributed lag non-linear models (DLNM),
quasi-Poisson time-series regression, case-crossover analysis,
Bayesian spatio-temporal models using the Integrated Nested Laplace
Approximation ('INLA'), and multivariate meta-analysis for
sub-national estimates. The package is based on methods developed
in the Standards for Official Statistics on Climate-Health
Interactions (SOSCHI) project
< https://climate-health.officialstatistics.org>. For methodologies,
see Watkins et al. (2026)
Climate Variability Indices for Ecological Modelling
Supports analysis of trends in climate change, ecological and crop modelling.
Access Climate Hazards Center Datasets
Provides access to Climate Hazards Center (CHC) datasets including CHIRPS v3, CHIRPS v2, CHIRTS-daily and CHIRTS-ERA5, read directly from the CHC server. 'CHIRPS' is a quasi-global (60°S to 60°N for v3) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S to 70°N), high-resolution data set of daily maximum and minimum temperatures. Data are returned as rasters or as time series for points and polygons, ready for climate indices with the 'climatrends' package. For more details on CHC datasets please visit their official home page < https://www.chc.ucsb.edu/data>.
'GAMS' Modularization Support Package
A collection of tools to create, use and maintain modularized model code written in the modeling language 'GAMS' (< https://www.gams.com/>). Out-of-the-box 'GAMS' does not come with support for modularized model code. This package provides the tools necessary to convert a standard 'GAMS' model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.
Analysis the Weather Data for Agriculture
Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, < https://www.longpaddock.qld.gov.au/silo/>).