This package provides functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).

Immune related gene sets provided along with the cinaR package.

Read and manipulate Camera Trap Data Packages ('Camtrap DP'). Camtrap DP (<https://camtrap-dp.tdwg.org>) is a data exchange format for camera trap data. With camtrapdp you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. camtraptor or camtrapR'.

This package provides an implementation of â Curricular Analyticsâ , a framework for analyzing and quantifying the complexity of academic curricula. Curricula are modelled as directed acyclic graphs and analytics are provided based on path lengths and edge density. This work directly comes from Heileman et al. (2018) <doi:10.48550/arXiv.1811.09676>.

Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).

Utilize the shiny interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the shiny session. Model diagnostics can be included in a rmarkdown document and rendered to desired output format.

Interact with Condor from R via SSH connection. Files are first uploaded from user machine to submitter machine, and the job is then submitted from the submitter machine to Condor'. Functions are provided to submit, list, and download Condor jobs from R. Condor is an open source high-throughput computing software framework for distributed parallelization of computationally intensive tasks.

An interactive document on the topic of confusion matrix analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ConfusionMatrixShiny/>.

This package provides a Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses.

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) <doi:10.1007/978-3-030-51924-7_4>.

Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.

This package provides functions to test and compare causal models using Confirmatory Path Analysis.

This package provides a set of common functions to be used for displaying messages, checking variables, finding absolute paths, starting applications, etc. More functions will be added later.

This package provides a set of tools that can be used across data.frame and imputationList objects.

Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.

Convolute probabilistic distributions using the random generator function of each distribution. A new random number generator function is created that perform the mathematical operation on the individual random samples from the random generator function of each distribution. See the documentation for examples.

Facilitates dynamic exploration of text collections through an intuitive graphical user interface and the power of regular expressions. The package contains 1) a helper function to convert a data frame to a corporaexplorerobject and 2) a Shiny app for fast and flexible exploration of a corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the janeaustenr and sotu packages respectively).

Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2> and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. See <https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . <https://cdriver.netlify.app/> contains some tutorial blog posts.

This package performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>. Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) <doi:10.1016/j.foodqual.2025.105639>.

Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.

This package performs copy number variants association analysis with Lasso and Weighted Fusion penalized regression. Creates a "CNV profile curve" to represent an individualâ s CNV events across a genomic region so to capture variations in CNV length and dosage. When evaluating association, the CNV profile curve is directly used as a predictor in the regression model, avoiding the need to predefine CNV loci. CNV profile regression estimates CNV effects at each genome position, making the results comparable across different studies. The penalization encourages sparsity in variable selection with a Lasso penalty and encourages effect smoothness between consecutive CNV events with a weighted fusion penalty, where the weight controls the level of smoothing between adjacent CNVs. For more details, see Si (2024) <doi:10.1101/2024.11.23.624994>.