Climate and Ecological Niche Factor Analysis

Tools for climate- and ecological-niche factor analysis of spatial data, including methods for visualization of spatial variability of species sensitivity, exposure, and vulnerability to climate change. Processing of large files and parallel methods are supported. Climate-niche factor analysis is described in Rinnan and Lawler (2019) .

Easily Download and Visualise Climate Data from CliFlo

CliFlo is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see < https://cliflo.niwa.co.nz/> for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought.

Homogeneity and Sparsity Detection Incorporating Prior Constraint Information

We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators.

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.

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.

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.

Simulation of Sediment Archived Climate Proxy Records

Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) .