This package provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.

This package provides tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel.

Compare directories flexibly (by date, content, or both) and synchronize files efficiently, with asymmetric and symmetric modes, helper tools, and visualization support for file management.

Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.

This package provides tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).

This package contains chart code for monitoring clinical trial safety. Charts can be used as standalone output, but are also designed for use with the safetyGraphics package, which makes it easy to load data and customize the charts using an interactive web-based interface created with Shiny.

Access Amazon Web Service Simple Storage Service ('S3') <https://aws.amazon.com/s3/> as if it were a file system. Interface based on the R package fs'.

An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

This package performs analysis of split-split plot experiments in both completely randomized and randomized complete block designs. With the results, you can obtain ANOVA, mean tests, and regression analysis (Este pacote faz a analise de experimentos em parcela subsubdivididas no delineamento inteiramente casualizado e delineamento em blocos casualizados. Com resultados e possà vel obter a ANOVA, testes de medias e análise de regressao) <https://www.expstat.com/pacotes-do-r>.

Shiny wrappers for the RGL package. This package exposes RGL's ability to export WebGL visualization in a shiny-friendly format.

Estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923>, Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>, Kim and Handcock (2021) <doi:10.1093/jssam/smz055>, and McLaughlin, et. al. (2023) <doi:10.1214/23-AOAS1807>.

This package provides a facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.

This package provides functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4> "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) <doi:10.1088/0967-3334/17/4/008> "Calculation of area of stabilometric signals using principal component analisys".

SqueezeMeta is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading SqueezeMeta results into R, filtering them based on different criteria, and visualizing the results using basic plots. The SqueezeMeta project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as vegan or DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.

This package provides a set of user interface components to create outstanding shiny apps <https://shiny.posit.co/>, with the power of React JavaScript <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with tailwind <https://tailwindcss.com/>.

Various tools for semantic vector spaces, such as correspondence analysis (simple, multiple and discriminant), latent semantic analysis, probabilistic latent semantic analysis, non-negative matrix factorization, latent class analysis, EM clustering, logratio analysis and log-multiplicative (association) analysis. Furthermore, there are specialized distance measures, plotting functions and some helper functions.

Capture screenshots in Shiny applications. Screenshots can either be of the entire viewable page, or a specific section of the page. The captured image is automatically downloaded as a PNG image, or it can also be saved on the server. Powered by the html2canvas JavaScript library.

It can be useful to temporarily hide some text or other HTML elements in Shiny applications. Building on Spoiler-Alert.js', it is possible to select the elements to hide at startup, to partially reveal them by hovering them, and to completely show them when clicking on them.

Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model.

Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.

This package provides a collection of classes and methods for working with times and dates. The code was originally available in S-PLUS'.

This package provides a set of tools inspired by Stata to explore data.frames ('summarize', tabulate', xtile', pctile', binscatter', elapsed quarters/month, lead/lag).

This package provides an XY pad input for the Shiny framework. An XY pad is like a bivariate slider. It allows to pick up a pair of numbers.