Fit and apply ComBat, linear mixed-effects models (LMM), or prescaling to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, these methods remove differences between sites due to using different scanning devices, and LMM additionally tests linear hypotheses. As detailed in the manual, the original ComBat function was first modified for the harmonization of MRI data (Fortin et al. (2017) <doi:10.1016/j.neuroimage.2017.11.024>) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) <doi:10.1016/j.neuroimage.2020.116956>); this package includes the latter version. LMM calls "lme" massively considering specific brain imaging details. Finally, prescaling is a good option for fMRI, where different devices can have varying units of measurement.

Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) <arXiv:1312.5921>. The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf> and Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html> for details on Bayesian CCA and GFA, respectively.

This package provides a wrapper for the CDRC API that returns data frames or sf of CDRC data. The API web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.

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

This package provides tools to simulate categorical raster data, including objects from the terra package. Functions focus on generating edge pixel values from reclassified raster data derived from the United States Department of Agriculture (USDA) Cropland Data Layer products.

Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using CIRCE (<https://github.com/ohdsi/circe-be>) or SQL compatible with SqlRender (<https://github.com/OHDSI/SqlRender>).

Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <doi:10.48550/arXiv.1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.

It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Additive copula regression for regression problems with binary outcome via gradient boosting [Brant, Hobæk Haff (2022); <arXiv:2208.04669>]. The fitting process includes a specialised model selection algorithm for each component, where each component is found (by greedy optimisation) among all the D-vines with only Gaussian pair-copulas of a fixed dimension, as specified by the user. When the variables and structure have been selected, the algorithm then re-fits the component where the pair-copula distributions can be different from Gaussian, if specified.

An implementation in base R of the CHAID (Chi-squared Automatic Interaction Detection) decision tree algorithm of Kass (1980) <doi:10.2307/2986296> and the Exhaustive CHAID variant of Biggs, de Ville, and Suen (1991) <doi:10.1080/02664769100000005>, as specified in the IBM SPSS Statistics Algorithms documentation. Supports nominal, ordinal (with floating missing category), and continuous predictors, and nominal, ordinal, and continuous response variables using Pearson chi-squared, Goodman row-effects, and one-way ANOVA F tests respectively. Includes prediction, rule extraction, gains and lift analysis, validation on holdout data, and visualization via base graphics, Graphviz DOT, plotly', and conversion to partykit objects.

Adjusts the loglikelihood of common econometric models for clustered data based on the estimation process suggested in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the chandwich package <https://cran.r-project.org/package=chandwich>, and provides convenience functions for inference on the adjusted models.

This package performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold.

This package provides access to the Calcite Design System javascript components via integration with the htmltools and shiny packages. Pre-built and interactive components can be used to generate either static html or interactive web applications. Learn more about the Calcite Design System at <https://developers.arcgis.com/calcite-design-system/>.

This package provides a new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.

Advanced climate simulation, forecasting, visualization, export, and machine learning tools. Generates synthetic climate datasets for single or multiple weather stations using stochastic weather generation techniques. CDSimX simulates daily climate variables including minimum and maximum temperature, rainfall, relative humidity, solar radiation, wind speed, wind direction, dew point temperature, and potential evapotranspiration. The package incorporates seasonal harmonic models, Markov chain rainfall occurrence processes, Gamma-distributed rainfall amounts, copula-based dependence structures, bias-correction procedures, and physical consistency constraints. CDSimX supports climate data generation, environmental modeling, machine learning benchmarking, sensitivity analysis, and educational applications. Methods are based on established stochastic weather generation approaches described in Richardson (1981) <doi:10.1029/WR017i001p00182>, Wilks (1999) <doi:10.1016/S0168-1923(99)00037-4>, and Osei et al. (2026) <doi:10.5334/jors.666>.

Parses compact numeric values, qualified quantities, and ranges commonly found in Chinese tables and spreadsheets. It handles Chinese magnitude suffixes, currencies, percentages, full-width characters, financial negatives, and configurable missing-value markers while reporting values that cannot be parsed safely.

Helps automate Quarto website creation for small academic groups. Builds a database-like structure of people, projects and publications, linking them together with a string-based ID system. Then, provides functions to automate production of clean markdown for these structures, and in-built CSS formatting using CSS flexbox.

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

Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.

Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools).

Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.

This package provides a workflow to generate and analyze signatures based on copy number data using non-negative matrix factorization (NMF) in an approach similar to that used in mutational signatures. It can be used to extract features from Copy number segment data and use that to find a subset of copy number signatures which can be further used to correlate with other relevant data. For more on NMF see Gaujoux (2013) <doi:10.1186/1471-2105-11-367>.

This package provides a common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition. See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report.