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.

Estimation of the Number of Fatalities from Carcass Searches

The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.

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 ).

Advanced Analysis of Longitudinal Data Using the Concordance Correlation Coefficient

Methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via 'nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) .

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/>.

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>.

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.

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) .