Build multiscalar territorial analysis based on various contexts.

Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework.

Process OpenPose human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA NewsScape archive.

This package implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.

This package provides a data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.

Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>, transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.

Mapping Averaged Pairwise Information (MAPI) is an exploratory method providing graphical representations summarizing the spatial variation of pairwise metrics (eg. distance, similarity coefficient, ...) computed between georeferenced samples.

This package provides functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].

This package provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.

An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

Framework for creating and orchestrating data pipelines. Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration.

Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - common effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - three-level meta-analysis model; - generalised linear mixed model; - logistic regression with penalised likelihood for rare events; - Hartung-Knapp method for random effects model; - Kenward-Roger method for random effects model; - prediction interval; - statistical tests for funnel plot asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; - meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; - import data from RevMan 5'; - produce forest plot summarising several (subgroup) meta-analyses.

Implementation of the Monothetic Clustering algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on continuous data sets. A lot of extensions are included in the package, including applying Monothetic clustering on data sets with circular variables, visualizations with the results, and permutation and cross-validation based tests to support the decision on the number of clusters.

This package provides methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.

This package produces clean and neat Markdown log file and also provide an argument to include the function call inside the Markdown log.

This package provides an interface with the Meteo France Synop data API (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> for more information). The Meteo France Synop data are made of meteorological data recorded every three hours on 62 French meteorological stations.

This package provides functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al. (2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.

This package provides functions for row-reducing and inverting matrices with entries in many of the finite fields (those with a prime number of elements). With this package, users will be able to find the reduced row echelon form (RREF) of a matrix and calculate the inverse of a (square, invertible) matrix.

An implementation of multiple maps t-distributed stochastic neighbor embedding (t-SNE). Multiple maps t-SNE is a method for projecting high-dimensional data into several low-dimensional maps such that non-metric space properties are better preserved than they would be by a single map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE. When projecting onto more than one map, multiple maps t-SNE estimates a set of latent weights that allow each point to contribute to one or more maps depending on similarity relationships in the original data. This implementation is a port of the original Matlab library by Laurens van der Maaten. See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>. This material is based upon work supported by the United States Air Force and Defense Advanced Research Project Agency (DARPA) under Contract No. FA8750-17-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and Defense Advanced Research Projects Agency. Distribution Statement A: Approved for Public Release; Distribution Unlimited.

An interface to the Microsoft 365 (formerly known as Office 365') suite of cloud services, building on the framework supplied by the AzureGraph package. Enables access from R to data stored in Teams', SharePoint Online and OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured Outlook email client, with the ability to send emails and manage emails and mail folders.