This package provides tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible.

Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.

Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.

This package creates complex heatmaps for single cell RNA-seq data that simultaneously display gene expression levels (as color intensity) and expression percentages (as circle sizes). Supports gene grouping, cell type annotations, and time point comparisons. Built on top of ComplexHeatmap and integrates with Seurat objects. For more details see Gu (2022) <doi:10.1002/imt2.43> and Hao (2024) <doi:10.1038/s41587-023-01767-y>.

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

This package creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.

The implementation to perform the geometric spatial point analysis developed in Hernández & Solàs (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the sf package. The package has methods to print and plot the results.

Modular, execution-oriented strategy workflows that transform market features, portfolio state, and dynamic quantitative analysis into actionable signals, target positions, portfolio adjustments, and order intents. The package emphasizes path-dependent historical simulation so strategy decisions remain consistent with evolving account state, execution assumptions, and market conditions.

This package provides functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.

Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.

Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers.

This package provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages moveHMM and marcher'. The segmentation method is a bivariate extension of Lavielle's method available in adehabitatLT (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in cghseg package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.

Utility functions for spectroscopy. 1. Functions to simulate spectra for use in teaching or testing. 2. Functions to process files created by LoggerPro and SpectraSuite software.

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (GÃ E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear GÃ E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Non-proportional hazard (NPH) is commonly observed in immuno-oncology studies, where the survival curves of the treatment and control groups show delayed separation. To properly account for NPH, several statistical methods have been developed. One such method is Max-Combo test, which is a straightforward and flexible hypothesis testing method that can simultaneously test for constant, early, middle, and late treatment effects. However, the majority of the Max-Combo test performed in clinical studies are unstratified, ignoring the important prognostic stratification factors. To fill this gap, we have developed an R package for stratified Max-Combo testing that accounts for stratified baseline factors. Our package explores various methods for calculating combined test statistics, estimating joint distributions, and determining the p-values.

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

This package provides step-by-step derivations of key results in mathematical statistics, including transformations of random variables, order statistics, and sampling distributions. The package combines analytical derivation with Monte Carlo simulation to compare theoretical and empirical results, facilitating deeper understanding of statistical theory and its computational implementation. The methods are motivated by standard treatments in mathematical statistics (Hogg, McKean, and Craig, 2019, ISBN: 9780134686991).

Create Upset plots using a combination of ggplot2 and patchwork'.

Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the fGarch package). All tests are implemented using the Rcpp package which ensures great performance of the code.

This package provides tools for generating dynamic and standardized economic narratives in R Markdown documents. The package is primarily designed for French-language statistical and economic publications. It includes functions to describe changes in levels, percentages, trends, accelerations and short-term economic developments using consistent linguistic rules. The package supports automated reporting workflows and reproducible economic writing. Fournit des outils permettant de générer des textes économiques dynamiques et standardisés dans des documents R Markdown. Le package est principalement conçu pour les publications statistiques et économiques en français. Il propose des fonctions permettant de décrire les évolutions de niveaux, de pourcentages, de tendances, d'accélérations et les évolutions conjoncturelles à l'aide de règles linguistiques homogènes. Le package facilite l'automatisation de la rédaction et la reproductibilité des publications économiques.