Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.

This package provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

This package provides tools to convert from specific formats to more general forms of spatial data. Using tables to store the actual entities present in spatial data provides flexibility, and the functions here deliberately minimize the level of interpretation applied, leaving that for specific applications. Includes support for simple features, round-trip for Spatial classes and long-form tables, analogous to ggplot2::fortify'. There is also a more normal form representation that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.

Identify 17 Sustainable Development Goals and associated 169 targets in text.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

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 system that computes metrics to assess the segmentation accuracy of geospatial data. These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.

Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.

It's a collection of functions for Multiplicity Correction and Multiple Testing.

This package provides functions common to members of the SISTM team.

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

Visualization and analysis of spatially resolved transcriptomics data. The spatialGE R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models.

This package provides a process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in C++ with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about simmer is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>); see citation("simmer") for details.

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Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) <doi:10.1017/pan.2019.49>. The model is built on a Bayesian framework in which the distinctiveness of each speaker is defined by how different, on average, the speaker's terms are to everyone else in the corpus of texts. An optional cross-validation method is implemented to select the subset of terms that generate the most accurate speaker predictions. Once a set of terms is selected, the model can be estimated. Speaker distinctiveness and term influence can be recovered from parameters in the model using package functions. Once fitted, the model can be used to predict authorship of new texts.

This package provides a customizable timer widget for shiny applications. Key features include countdown and count-up mode, multiple display formats (including simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), ability to pause, resume, and reset the timer. shinytimer widget can be particularly useful for creating interactive and time-sensitive applications, tracking session times, setting time limits for tasks or quizzes, and more.

Label, recode, rename, and convert datasets and ASCII files more efficiently. speedycode automates the code necessary for labeling variables with the labelled package, recoding and renaming variables with dplyr syntax, and converting ASCII files with the readroper package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.

Web application using shiny for the SSD (Species Sensitivity Distribution) module of the MOSAIC (MOdeling and StAtistical tools for ecotoxICology) platform. It estimates the Hazardous Concentration for x% of the species (HCx) from toxicity values that can be censored and provides various plotting options for a better understanding of the results. See our companion paper Kon Kam King et al. (2014) <doi:10.48550/arXiv.1311.5772>.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

In population management, data come at more or less regular intervals over time in sampling batches (bouts) and decisions should be made with the minimum number of samples and as quickly as possible. This package provides tools to implement, produce charts with stop lines, summarize results and assess sequential analyses that test hypotheses about population sizes. Two approaches are included: the sequential test of Bayesian posterior probabilities (Rincon, D.F. et al. 2025 <doi:10.1111/2041-210X.70053>), and the sequential probability ratio test (Wald, A. 1945 <http://www.jstor.org/stable/2235829>).

This package provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The rjags package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Extension to the spatstat package, containing interactive graphics capabilities.

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>.