STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.

This package provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back to XML code. It's not as powerful as the XML package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for the R GUI and IDE RKWard <https://rkward.kde.org>, to make plugin development easier.

An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers.

This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM.

This package provides a replacement and extension of the optim function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that the function optimr was prepared to simplify the incorporation of minimization codes going forward. This package also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.

This package provides classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>. Provides a collection of functions to perform biological conversion among classes (transcription, translation) and basic operations on sequences (detection, selection and replacement based on positions or patterns). The package also provides functions to import and export sequences from and to other package formats.

Working with reproducible reports or any other similar projects often require to run the script that builds the output file in a specified way. buildr can help you organize, modify and comfortably run those scripts. The package provides a set of functions that interactively guides you through the process and that are available as RStudio Addin, meaning you can set up the keyboard shortcuts, enabling you to choose and run the desired build script with one keystroke anywhere anytime.

Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.

Computes a structural similarity metric (after the style of MS-SSIM for images) for binary and categorical 2D and 3D images. Can be based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted Rand index, Jaccard index, Dice index, normalized mutual information, or adjusted mutual information. In addition, has fast computation of Cohen's kappa, the Rand indices, and the two mutual informations. Implements the methods of Thompson and Maitra (2020) <doi:10.48550/arXiv.2004.09073>.

The D-score summarizes the child's performance on a set of milestones into a single number. The package implements four Rasch model keys to convert milestone scores into a D-score. It provides tools to calculate the D-score and its precision from the child's milestone scores, to convert the D-score into the Development-for-Age Z-score (DAZ) using age-conditional references, and to map milestone names into a generic 9-position item naming convention.

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Automatically adding pkg:: to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the RStudio Markers pane. Lives in the spirit of lintr and styler'. Can also be used for checking which packages are actually used in a project.

This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.

This package provides a set of tools that enables using OxCal from within R. OxCal (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. OxcAAR allows simple calibration with Oxcal and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.

Loads and processes huge text corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.

This package provides a robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2023), <https://www.biorxiv.org/content/10.1101/2023.05.30.542607v1>].

This package is an R implementation for fully unsupervised deconvolution of complex tissues. DebCAM provides basic functions to perform unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation- specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.

This package provides functions, data sets, analyses and examples from the third edition of the book A Handbook of Statistical Analyses Using R (Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014). The first chapter of the book, which is entitled An Introduction to R, is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, Sweave source code for slides of selected chapters is included in this package.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables.

This package provides a free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell anabel which method to use, and it takes care of all fitting details. The first two releases of anabel were created and implemented as in (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: rstan', INLA', spBayes', spTimer', spTDyn', CARBayes and CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 2015 as existing Bland-Altman analysis functions did not calculate confidence intervals. This package was created to rectify this, and create reproducible plots. This package is also available as a module for the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information).

This package provides a simple R wrapper for the Java BiBit algorithm from "A biclustering algorithm for extracting bit-patterns from binary datasets" from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.

Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.