Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.

This package provides a pipeline that can process single or multiple Single Cell RNAseq samples primarily specializes in Clustering and Dimensionality Reduction. Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also.

In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.

This package provides a toolbox for defining React component wrappers which can be used seamlessly in Shiny apps.

Plots a QQ-Norm Plot with several Gaussian simulations.

SigClust is a statistical method for testing the significance of clustering results. SigClust can be applied to assess the statistical significance of splitting a data set into two clusters. For more than two clusters, SigClust can be used iteratively.

This package contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, <doi:10.1214/20-BA1227>). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly shiny interface.

Do multi-gene descent probabilities (Thompson, 1983, <doi:10.1098/rspb.1983.0072>) and special cases thereof (Thompson, 1986, <doi:10.1002/zoo.1430050210>) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes.

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. stacks implements a grammar for tidymodels'-aligned model stacking.

Simulates and plots quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models. It also simulates and plots marginal effects for multiplicative interactions. Methods described in Gandrud (2015) <doi:10.18637/jss.v065.i03>.

The goal of the SwimmeR package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end SwimmeR allows results to be read in from .html sources, like Hy-Tek real time results pages, .pdf files, ISL results, Omega results, and (on a development basis) .hy3 files. Once read in, SwimmeR can convert swimming times (performances) between the computationally useful format of seconds reported to the 100ths place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. SwimmeR can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', SCM', SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.

Seamless and standardized interaction with data exported from the clinical data management system (CDMS) secuTrial'<https://www.secutrial.com>. The primary data export the package works with is a standard non-rectangular export.

This package provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.

Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of Rcpp and RcppArmadillo'.

This package implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.

This package performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).

This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.

This package provides tools for interacting with U.S. Geological Survey ScienceBase <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and collaborative data management platform. Functions included for querying ScienceBase, and creating and fetching datasets.

This package provides functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors.

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Manipulating input and output files of the STICS crop model. Files are either JavaSTICS XML files or text files used by the model fortran executable. Most basic functionalities are reading or writing parameter names and values in both XML or text input files, and getting data from output files. Advanced functionalities include XML files generation from XML templates and/or spreadsheets, or text files generation from XML files by using xslt transformation.

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) <arXiv:1812:09384>.