Temporal Trends in Ecological Niche Models

Computes temporal trends in environmental suitability obtained from ecological niche models, based on a set of species presence point coordinates and predictor variables.

Auxiliary Functions to Estimate Centers of Biodiversity

Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

Download Data from the Wittgenstein Centre Human Capital Data Explorer

Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer < https://dataexplorer.wittgensteincentre.org/>.

Blind Source Separation for Multivariate Spatio-Temporal Data

Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.

Simple Tools for Defining Species Ranges

A collection of tools to create species range maps based on occurrence data, statistics, and spatial objects. Other tools in this collection can be used to analyze the environmental characteristics of the species ranges. Plotting options to represent results in various manners are also available. Results obtained using these tools can be used to explore the distribution of species and define areas of occupancy and extent of occurrence of species. Other packages help to explore species distributions using distinct methods, but options presented in this set of tools (e.g., using trend surface analysis and concave hull polygons) are exclusive. Description of methods, approaches, and comments for some of the tools implemented here can be found in: IUCN (2001) < https://portals.iucn.org/library/node/10315>, Peterson et al. (2011) < https://www.degruyter.com/princetonup/view/title/506966>, and Graham and Hijmans (2006) .

A Phylogenetic Simulator for Reticulate Evolution

A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization

Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at < https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.

Methods for Spatial Downscaling Using Deep Learning

The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 ) and UNet (Ronneberger et al., 2015 ), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 ). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) .

Test Data for the 'admiral' Package

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the 'admiral' package.

Blind Source Separation for Multivariate Spatial Data

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) .