Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.

This package is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. r-fourcseq provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a Python script to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated.

The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides:

1. low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and

2. a framework that facilitates block processing of array-like objects (typically on-disk objects).

This package provides tools to compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples.

dominatR is an R package for quantifying and visualizing feature dominance in datasets. dominatR applies concepts drawn from physics such as center of mass and shannon's entropy to effectively visualize features (e.g. genes) that are present within a specific context or condition. The package integrates, dataframes, matrices and SummerizedExperiment objects and is able to perform common genomic normalization methods. The key aspect is the generation of plots that serve to highlight context-relevant feature dominance.

LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks.

Verifies that an array covers every t-way interaction, the certificate check for covering arrays, using compiled C code with optional OpenMP threading. Supports uniform and mixed-level (per-column symbol counts) arrays. Missing values mark flexible ("don't care") entries, which contribute nothing to coverage, so a verified array remains covering however they are filled. Designed to be easy to embed in other packages: a single C file with a registered .Call entry point and one R wrapper.

Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the INLA framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) <doi:10.1016/S2542-5196(20)30292-8>.

It can be necessary to limit the rate of execution of a loop or repeated function call e.g. to show or gather data only at particular intervals. This package includes two methods for limiting this execution rate; speed governors and timers. A speed governor will insert pauses during execution to meet a user-specified loop time. Timers are alarm clocks which will indicate whether a certain time has passed. These mechanisms are implemented in C to minimize processing overhead.

Group SLOPE (Group Sorted L1 Penalized Estimation) is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a high-dimensional linear model. The Group SLOPE method can control the (group) false discovery rate at a user-specified level (i.e., control the expected proportion of irrelevant among all selected groups of predictors). For additional information about the implemented methods please see Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.

Duct tape the quanteda ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of quanteda.textmodels and provides a function to setup the Python environment to use the pretrained models from Hugging Face <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

This package provides a fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the IDSL.IPA workflow <doi:10.1021/acs.jproteome.2c00120>. The IDSL.CSA package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The IDSL.CSA package was introduced in <doi:10.1021/acs.analchem.3c00376>.

Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome.

Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.

This package provides a Shiny graphical interface for the complete workflow of Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from Scopus and Web of Science. Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots.

Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density function. Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.

This package provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008>), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>).

This package provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.

This package implements the American Heart Association Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan SS, Matsushita K, Sang Y, and colleagues (2023) <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison with their de facto predecessor, the Pooled Cohort Equations from the American Heart Association and American College of Cardiology (2013) <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled Cohort Equations from Yadlowsky and colleagues (2018) <doi:10.7326/M17-3011>.

Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway.

This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture.

For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease, we developed this program to acquire disease-related genes. The data is integrated from three public databases. The three databases are eDGAR', DrugBank and MalaCards'. The eDGAR is a comprehensive database, containing data on the relationship between disease and genes. DrugBank contains information on 13443 drugs and 5157 targets. MalaCards integrates human disease information, including disease-related genes.

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.