We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The rjags package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).

InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions.

Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.

This package implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package scipy.cluster.hierarchy, hclust() in R's stats package, and the flashClust package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide.

This package computes optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

This package provides methods to deal with the free antiassociative algebra over the reals with an arbitrary number of indeterminates. Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, <doi:10.48550/arXiv.2202.10812>) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided.

This package provides more than 550 data sets of actual election results. Each of the data sets includes aggregate party and candidate outcomes at the voting unit (polling stations) level and two-way cross-tabulated results at the district level. These data sets can be used to assess ecological inference algorithms devised for estimating RxC (global) ecological contingency tables using exclusively aggregate results from voting units. Reference: Pavà a (2022) <doi:10.1177/08944393211040808>.

The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.

This package provides a comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users convenience, the functions also provide the output option for printing decision rules.

This package provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the batchtools package) making it flexible enough for use in large-scale data analysis projects.

Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) <doi:10.1002/ajpa.1330660105>, Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.

The imcdatasets package provides access to publicly available IMC datasets. IMC is a technology that enables measurement of > 40 proteins from tissue sections. The generated images can be segmented to extract single cell data. Datasets typically consist of three elements: a SingleCellExperiment object containing single cell data, a CytoImageList object containing multichannel images and a CytoImageList object containing the cell masks that were used to extract the single cell data from the images.

Statistics implemented for both peak-wise and gene-wise associations. In peak-wise associations, the p-value of the target genes of a given set of peaks are calculated. Negative binomial or Poisson distributions can be used for modeling the unweighted peaks targets and log-nromal can be used to model the weighted peaks. In gene-wise associations a table consisting of a set of genes, mapped to specific peaks, is generated using the given rules.

This package is used for the detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. It also provides the possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots.

An interface to Azure Computer Vision <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and Azure Custom Vision <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the AzureCognitive package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the AzureR family of packages.

This package provides a set of user-friendly functions designed to fill gaps in existing introductory biostatistics R tools, making it easier for newcomers to perform basic biostatistical analyses without needing advanced programming skills. The methods implemented in this package are based on the works: Connor (1987) <doi:10.2307/2531961> Fleiss, Levin, & Paik (2013, ISBN:978-1-118-62561-3) Levin & Chen (1999) <doi:10.1080/00031305.1999.10474431> McNemar (1947) <doi:10.1007/BF02295996>.

Variable selection for Gaussian model-based clustering as implemented in the mclust package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting mclust models. By default the algorithm uses a sequential search, but parallelisation is also available.

This package provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), and test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).

Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) ReliefWeb disaster events at <https://reliefweb.int/disasters>. The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow.

This package provides tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.

This package provides tools for estimate (joint) cumulants and (joint) products of cumulants of a random sample using (multivariate) k-statistics and (multivariate) polykays, unbiased estimators with minimum variance. Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) <arXiv:0807.5008>, <arXiv:1012.6008>.

L-systems or Lindenmayer systems are parallel rewriting systems which can be used to simulate biological forms and certain kinds of fractals. Briefly, in an L-system a series of symbols in a string are replaced iteratively according to rules to give a more complex string. Eventually, the symbols are translated into turtle graphics for plotting. Wikipedia has a very good introduction: en.wikipedia.org/wiki/L-system This package provides basic functions for exploring L-systems.

The mlrMBO package can ordinarily not be used for optimization within mlr3', because of incompatibilities of their respective class systems. mlrintermbo offers a compatibility interface that provides mlrMBO as an mlr3tuning Tuner object, for tuning of machine learning algorithms within mlr3', as well as a bbotk Optimizer object for optimization of general objective functions using the bbotk black box optimization framework. The control parameters of mlrMBO are faithfully reproduced as a paradox ParamSet'.

This package provides quality control (QC), normalization, and batch effect correction operations for NanoString nCounter data, Talhouk et al. (2016) <doi:10.1371/journal.pone.0153844>. Various metrics are used to determine which samples passed or failed QC. Gene expression should first be normalized to housekeeping genes, before a reference-based approach is used to adjust for batch effects. Raw NanoString data can be imported in the form of Reporter Code Count (RCC) files.