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.

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.

Climate Variability Indices for Ecological Modelling

Supports analysis of trends in climate change, ecological and crop modelling.

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.

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. (2025) , Brown et al. (2024) , Pearce et al. (2024) , Byukusenge et al. (2025) , Dzakpa et al. (2025) , and Dzakpa et al. (2025) .

'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.

May All Data be Reproducible and Transparent (MADRaT) *

Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation. * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.

Visualize the Climate Scenario Alignment of a Financial Portfolio

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the 'PACTA' methodology (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.

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.