This package provides Sensory and Consumer Data mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A shiny application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available.

This package implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the BayesTree package.

This package provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) <doi:10.1214/aoms/1177731118>, who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term SPRT is an abbreviation and used intentionally.

Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.

This package provides a set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.

This package provides functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package.

Generates synonyms from a given word drawing from a synonym list from the moby project <http://moby-thesaurus.org/>.

For Multi Parent Populations (MPP) Identity By Descend (IBD) probabilities are computed using Hidden Markov Models. These probabilities are then used in a mixed model approach for QTL Mapping as described in Li et al. (<doi:10.1007/s00122-021-03919-7>).

This package performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.

Sequential triangular test for the arithmetic mean in one- and two- samples, proportions in one- and two-samples, and the Pearson's correlation coefficient.

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.

Sometimes it is handy to be able to view an image file on an R graphics device. This package just does that. Currently it supports PNG files.

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>.

Companion package that supports the surveydown survey platform (<https://surveydown.org>). The default method for working with a surveydown survey is to edit the plain text survey.qmd and app.R files. With sdstudio', you can create, preview and manage surveys with a shiny application as a graphical user interface.

This package provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage C++ for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

This package provides a future backend that enables seamless execution of parallel R workloads on Amazon Web Services ('AWS', <https://aws.amazon.com>), including EC2 and Fargate'. staRburst handles environment synchronization, data transfer, quota management, and worker orchestration automatically, allowing users to scale from local execution to 100+ cloud workers with a single line of code change.

This package provides a set of statistical tools for spatio-temporal data exploration. Includes simple plotting functions, covariance calculations and computations similar to principal component analysis for spatio-temporal data. Can use both dataframes and stars objects for all plots and computations. For more details refer Spatio-Temporal Statistics with R (Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).

This package provides a comprehensive framework for descriptive statistics and regression analysis that produces publication-ready tables and forest plots. Provides a unified interface from descriptive statistics through multivariable modeling, with support for linear models, generalized linear models, Cox proportional hazards, and mixed-effects models. Also includes univariable screening, multivariate regression, model comparison, and export to multiple formats including PDF, DOCX, PPTX, LaTeX', HTML, and RTF. Built on data.table for computational efficiency.

This package provides a user-friendly wrapper for web automation, using either chromote or selenium'. Provides a simple and consistent API to make web scraping and testing scripts easy to write and understand. Elements are lazy, and automatically wait for the website to be valid, resulting in reliable and reproducible code, with no visible impact on the experience of the programmer.

This package provides an easy-to-use module for adding a chat to a Shiny app. Allows users to send messages and view messages from other users. Messages can be stored in a database or a .rds file.

This package provides a collection of simple parameter estimation and tests for the comparison of multivariate means and variation, to accompany Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer (5th edition), by Manly BFJ, Navarro Alberto JA & Gerow K (2024) <doi:10.1201/9781003453482>.

This package provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.