Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using am Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).

This package provides a toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. SimNPH encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the SimDesign package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

This is the human disease ontology R package HDO.db, which provides the semantic relationship between human diseases. Relying on the DOSE and GOSemSim packages, this package can carry out disease enrichment and semantic similarity analyses. Many biological studies are achieved through mouse models, and a large number of data indicate the association between genotypes and phenotypes or diseases. The study of model organisms can be transformed into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Organism-specific genotype-phenotypic associations can be applied to cross-species phenotypic studies to clarify previously unknown phenotypic connections in other species. Using the same principle to diseases can identify genetic associations and even help to identify disease associations that are not obvious.

EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs.

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

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>.

Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node. Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure. Traversal means visiting all the nodes of a graph. Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria. It starts at the treeâ s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. Also, it provides the matrix which is reachable between each node. Implement reference about Baruch Awerbuch (1985) <doi:10.1016/0020-0190(85)90083-3>.

Easy implementation of the MABAC multi-criteria decision method, that was introduced by PamuÄ ar and Ä iroviÄ in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - <doi:10.1016/j.eswa.2014.11.057> - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated.

The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the best fit on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.

This package provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.

Single-index mixture cure models allow estimating the probability of cure and the latency depending on a vector (or functional) covariate, avoiding the curse of dimensionality. The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (<https://ruc.udc.es/dspace/handle/2183/37035>). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health.

How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which tidylo provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available.

The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, <doi:10.1080/01621459.2025.2606381>). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.

This package implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.

Implementation of adaptive assessment procedures based on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 <ISBN:9783540645016>) and Formal Psychological Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 <doi:10.3758/BRM.42.1.342>) frameworks. An adaptive assessment is a type of evaluation that adjusts the difficulty and nature of subsequent questions based on the test taker's responses to previous ones. The package contains functions to perform and simulate an adaptive assessment. Moreover, it is integrated with two Shiny interfaces, making it both accessible and user-friendly. The package has been partially funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project â RAISE - Robotics and AI for Socio-economic Empowermentâ (ECS00000035).

It is often useful to produce short, quasi-unique identifiers (SQUIDs) without the benefit of a central authority to prevent duplication. Although Universally Unique Identifiers (UUIDs) provide for this, these are also unwieldy; for example, the most used UUID, version 4, is 36 characters long. SQUIDs are short (8 characters) at the expense of having more collisions, which can be mitigated by combining them with human-produced suffixes, yielding relatively brief, half human-readable, almost-unique identifiers (see for example the identifiers used for Decentralized Construct Taxonomies; Peters & Crutzen, 2024 <doi:10.15626/MP.2022.3638>). SQUIDs are the number of centiseconds elapsed since the beginning of 1970 converted to a base 30 system. This package contains functions to produce SQUIDs as well as convert them back into dates and times.

Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.

Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets.

This package provides a collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.

This is a cross-platform linear model to SQL compiler. It generates SQL from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a SQL character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant SQL can be included in a SELECT statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.

This package performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

This package provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386> as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) <doi:10.1007/s10182-025-00545-2>.

Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to BEESTS software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.