This package provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.

Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

Paternal recombination rate and maternal linkage disequilibrium (LD) are estimated for pairs of biallelic markers such as single nucleotide polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The implementation relies on paternal half-sib families. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. For parameter estimation, at least one sire has to be double heterozygous at the investigated pairs of SNPs. Based on recombination rates, genetic distances between markers can be estimated. Markers with unusually large recombination rate to markers in close proximity (i.e. putatively misplaced markers) shall be discarded in this derivation. A workflow description is attached as vignette. *A pipeline is available at GitHub* <https://github.com/wittenburg/hsrecombi> Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of recombination rate and maternal linkage disequilibrium in half-sibs" <doi:10.3389/fgene.2018.00186>. Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium in half-sib families" <doi:10.1534/genetics.111.137521>.

This package provides a comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see <https://hcup-us.ahrq.gov/>.

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>. Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.

This package performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

This package provides a tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. TransProR is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include limma (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), edgeR (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), DESeq2 (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of ZetaSuite in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.

This package provides a collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The BigCamelCase style was consequently applied to functions borrowed from contributed R packages as well.

FamSKAT-RC is a family-based association kernel test for both rare and common variants. This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.

Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm lab2clean as an open-source R-package. lab2clean package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in Zayed et al. (2024) <doi:10.1186/s12911-024-02652-7>. Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in Zayed et al. (2025) <doi:10.1016/j.ijmedinf.2025.106131>.

We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.

This package implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values. Bhoj, D. S. (1991) <doi:10.1002/bimj.4710330108> "Testing equality of means in the presence of correlation and missing data". Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) <doi:10.22237/jmasm/1020254460> "Rank-based procedures for mixed paired and two-sample designs". Einsporn, R. L. and Habtzghi, D. (2013) <https://pdfs.semanticscholar.org/89a3/90bafeb2bc41ed4414533cfd5ab84a6b54b6.pdf> "Combining paired and two-sample data using a permutation test". Ekbohm, G. (1976) <doi:10.1093/biomet/63.2.299> "On comparing means in the paired case with incomplete data on both responses". Lin, P. E. and Stivers, L. E. (1974) <doi:10.1093/biomet/61.2.325> On difference of means with incomplete data". Maritz, J. S. (1995) <doi:10.1111/j.1467-842x.1995.tb00649.x> "A permutation paired test allowing for missing values".

Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) <doi:10.1111/2041-210X.14065>. We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) <doi:10.1086/681104>. For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) <doi:10.1371/journal.pcbi.1004771>.

Carries out a two-level sample selection where the possibility of an initially selected site not wanting to participate is anticipated, and the site is optimally replaced. The procedure aims to reduce bias (and/or loss of external validity) with respect to the target population. In selecting units and sub-units, sitepickR uses the cube method developed by Deville & Tillé', (2004) <http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf> and described in Tillé (2011) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>. The cube method is a probability sampling method that is designed to satisfy criteria for balance between the sample and the population. Recent research has shown that this method performs well in simulations for studies of educational programs (see Fay & Olsen (2021, under review). To implement the cube method, sitepickR uses the sampling R package <https://cran.r-project.org/package=sampling>. To implement statistical matching, sitepickR uses the MatchIt R package <https://cran.r-project.org/package=MatchIt>.

It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as warabandi and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.

This package implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.

This package implements efficient NumPy'-like broadcasted operations for atomic and recursive arrays. In the context of operations involving 2 (or more) arrays, â broadcastingâ refers to efficiently recycling array dimensions, without making copies. Besides linking to Rcpp', broadcast does not use any external libraries in any way; broadcast was essentially made from scratch and can be installed out-of-the-box. The implementations available in broadcast include, but are not limited to, the following. 1) Broadcasted element-wise operations on any 2 arrays; they support a large set of relational, arithmetic, Boolean, string, and bit-wise operations. 2) A faster, more memory efficient, and broadcasted abind-like function, for binding arrays along an arbitrary dimension. 3) Broadcasted ifelse-like and apply-like functions. 4) Casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa. 5) A few linear algebra functions for statistics. The functions in the broadcast package strive to minimize computation time and memory usage (which is not just better for efficient computing, but also for the environment).

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, psborrow2 provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, psborrow2 provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, psborrow2 provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from cmdstanr which can be installed from <https://stan-dev.r-universe.dev>.

In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. TITEgBOIN provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. TITEgBOIN can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.

Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation WLMC (Fernández-Macho 2018) and the dominant variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martà nez et al. 2020, Polanco-Martà nez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the dominant variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in VisualDom are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named rdata_climate and rdata_Lorenz') to exemplify the use of the functions contained in VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martà nez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martà nez (2023, in press).