This package provides a Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the BoolNet package, which is licensed under the Artistic License 2.0.

Plot malaria parasite genetic data on two or more episodes. Compute per-person posterior probabilities that each Plasmodium vivax (Pv) recurrence is a recrudescence, relapse, or reinfection (3Rs) using per-person P. vivax genetic data on two or more episodes and a statistical model described in Taylor, Foo and White (2022) <doi:10.1101/2022.11.23.22282669>. Plot per-recurrence posterior probabilities.

This package provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. It also includes functions for simulating from and fitting ordinary hidden Markov models.

This package contains a graphical user interface to generate the diagnostic plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs).

Piecewise constant hazard models for survival data. The package allows for right-censored, left-truncated, and interval-censored data.

Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.

Make statistical inference on the probability of being in response, the duration of response, and the cumulative response rate up to a given time point. The method can be applied to analyze phase II randomized clinical trials with the endpoints being time to treatment response and time to progression or death.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

Enables user to perform the following: 1. Roll n number of die/dice (roll()). 2. Toss n number of coin(s) (toss()). 3. Play the game of Rock, Paper, Scissors. 4. Choose n number of card(s) from a pack of 52 playing cards (Joker optional).

This package provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through tidyLPA (Rosenberg et al., 2018) <doi:10.21105/joss.00978>, LCA through poLCA (Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> & glca (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>, LTA/IRT via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>, and EFA via psych (Revelle, 2025). SEM and CFA functionalities build upon the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming.

Easy function for text-mining the PubMed repository based on defined sets of terms. The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus) is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/> A text file is generated with the PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term), a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.

This package provides a user interface to create or modify pharmacometric models for various modeling and simulation software platforms.

An implementation of a formal grammar and parser for R Markdown documents using the Boost Spirit X3 library. It also includes a collection of high level functions for working with the resulting abstract syntax tree.

Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. You can pin objects to a variety of "boards", including local folders (to share on a networked drive or with DropBox'), Posit Connect', AWS S3', and more.

This package provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.

Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) <https://hal.archives-ouvertes.fr/hal-02002275>. It can be used with univariate and multivariate data.

This package provides a collection of functions that can be used to estimate selection and complementarity effects, sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.

This package implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.

Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>. US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.

Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.

R has no built-in pointer functionality. The pointr package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.

Streamline the creation of Docker images with R packages and dependencies embedded. The pracpac package provides a usethis'-like interface to creating Dockerfiles with dependencies managed by renv'. The pracpac functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.