Pre-Process, Visualize and Analyse Spectral Data

Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package.

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.

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) .