Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters.

The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.

This package implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 <doi:10.1007/s10985-015-9353-9>. Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified markâ specific proportional hazards models with missing marks. Scandinavian Journal of Statistics

Enforcement of field types in lists. A drop-in tool to allow for dynamic input data that might be questionably parsed or cast to be coerced into the specific desired format in a reasonably performant manner.

Implementations of the Single Transferable Vote counting system. By default, it uses the Cambridge method for surplus allocation and Droop method for quota calculation. Fractional surplus allocation and the Hare quota are available as options.

This package performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.

This package provides a simple HTTP server allows to connect GUI clients to R.

Computes confidence intervals for variance using the Chi-Square distribution, without requiring raw data. Wikipedia (2025) <https://en.wikipedia.org/wiki/Chi-squared_distribution>. All-in-One Chi Distribution CI provides functions to calculate confidence intervals for the population variance based on a chi-squared distribution, utilizing a sample variance and sample size. It offers only a simple all-in-one method for quick calculations to find the CI for Chi Distribution.

This package provides a helper function, to bulk read SQL code from separate files and load it into an R list, where the list elements contain the individual statements and queries as strings. This works by annotating the SQL code with a name comment, which also will be the name of the list element.

This package provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" <doi:10.1002/aic.690380502>). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" <doi:10.1002/9780470316757>). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results.

Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.

Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation. The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings. So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.

Manages and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.

This package implements Multivariate ANalysis Of VAriance (MANOVA) parameters inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI <doi:10.1002/bimj.70054>, arXiv DOI <doi:10.48550/arXiv.2401.04036>.

This package provides datasets from Vigen (2015) <https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations> rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via keras and tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from Python via reticulate <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using Rcpp <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the Python package pycox <https://github.com/havakv/pycox>.

Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and Luo et al. (2023) <doi:10.1177/09622802231181471>.

This package implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the caret package, other packages, or custom functions.

This package provides plotting utilities supporting packages in the easystats ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) <doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) <doi:10.1007/s00357-022-09428-6>.

Make R data available in Web-based virtual reality experiences for immersive, cross-platform data visualizations. Includes the gg-aframe JavaScript package for a Grammar of Graphics declarative HTML syntax to create 3-dimensional data visualizations with Mozilla A-Frame <https://aframe.io>.

This package provides a collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.

This package provides a very nice interface to Princeton's WordNet without rJava dependency. WordNet data is not included. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).