Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC.

Estimation of changepoints using an "S-curve" approximation. Formation of confidence intervals for changepoint locations and magnitudes. Both abrupt and gradual changes can be modeled.

Enhancing T cell receptor (TCR) sequence analysis, ClusTCR2', based on ClusTCR python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, ClusTCR2 is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) <doi:10.1093/bioinformatics/btab446>, Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) <doi:10.1101/2023.09.27.559702>.

This package provides a time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

This package provides a minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.

This package provides a programmatic interface to the Chromosome Counts Database (<https://ccdb.tau.ac.il/>), Rice et al. (2014) <doi:10.1111/nph.13191>. This package is part of the ROpenSci suite (<https://ropensci.org>).

Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.

This package implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Streamline the management, analysis, and visualization of CORINE Land Cover data. Addresses challenges associated with its classification system and related styles, such as color mappings and descriptive labels.

Spatial regression models with compositional responses using the alpha--transformation. Relevant papers include: Tsagris M. (2025), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>.

Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models.

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.

Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations. Please cite the following article when utilizing this package: Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) <doi:10.3389/fnins.2022.969510>.

This package implements a Ward-like hierarchical clustering algorithm including soft spatial/geographical constraints.

This package performs Correlated Meta-Analysis ('corrmeta') across multiple OMIC scans, accounting for hidden non-independencies between elements of the scans due to overlapping samples, related samples, or other information. For more information about the method, refer to the paper Province MA. (2013) <doi:10.1142/9789814447973_0023>.

This package provides a collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.

Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.

Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing clintrialx - Fetch clinical trial data from sources like ClinicalTrials.gov <https://clinicaltrials.gov/> and the Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!

This package provides a tool that imports, subsets, visualizes, and exports the Correlates of State Policy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more.

Create simplex plots to visualize the similarity between single-cells and selected clusters in a 1-/2-/3-simplex space. Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) <doi:10.1093/bioinformatics/btaf119> for more details.

This package implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) <doi:10.1002/sim.6097>.

In metabolic flux experiments tracer molecules (often glucose containing labelled carbon) are incorporated in compounds measured using mass spectrometry. The mass isotopologue distributions of these compounds needs to be corrected for natural abundance of labelled carbon and other effects, which are specific on the compound and ionization technique applied. This package provides functions to correct such effects in gas chromatography atmospheric pressure chemical ionization mass spectrometry analyses.