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.

Semi-Supervised Model for Geographical Document Classification

Semissupervised model for geographical document classification (Watanabe 2018) . This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic, Turkish, Japanese and Chinese (Simplified and Traditional).

Tools Developed by the Long Term Ecological Research Community

Set of the data science tools created by various members of the Long Term Ecological Research (LTER) community. These functions were initially written largely as standalone operations and have later been aggregated into this package.

Longitudinal Concordance Correlation

Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) . In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.

Generalized Spatial-Time Sequence Miner

Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 ).

Create Network Connections Based on Chess Moves

Provides functions to work with directed (asymmetric) and undirected (symmetric) spatial networks. It makes the creation of connectivity matrices easier, i.e. a binary matrix of dimension n x n, where n is the number of nodes (sampling units) indicating the presence (1) or the absence (0) of an edge (link) between pairs of nodes. Different network objects can be produced by 'chessboard': node list, neighbor list, edge list, connectivity matrix. It can also produce objects that will be used later in Moran's Eigenvector Maps (Dray et al. (2006) ) and Asymetric Eigenvector Maps (Blanchet et al. (2008) ), methods available in the package 'adespatial' (Dray et al. (2023) < https://CRAN.R-project.org/package=adespatial>). This work is part of the FRB-CESAB working group Bridge < https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/bridge/>.

Multiple Imputation for Proteomics

A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) . It is dedicated to dealing with multiple imputation for proteomics.

Make 'Mirai' 'Promises'

Allows 'mirai' objects encapsulating asynchronous computations, from the 'mirai' package by Gao (2023) , to be used interchangeably with 'promise' objects from the 'promises' package by Cheng (2021) < https://CRAN.R-project.org/package=promises>. This facilitates their use with packages 'plumber' by Schloerke and Allen (2022) < https://CRAN.R-project.org/package=plumber> and 'shiny' by Cheng, Allaire, Sievert, Schloerke, Xie, Allen, McPherson, Dipert and Borges (2022) < https://CRAN.R-project.org/package=shiny>.

Analysis and Identification of Raman Spectra of Microplastics

Pre-processing and polymer identification of Raman spectra of plastics. Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), ), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants.

Procrustes Matching for Latent Space Item Response Model

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) , and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) .