This package provides tools for shoreline dating coastal Stone Age sites. The implemented method was developed in Roalkvam (2023) <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. Although it can be extended to other areas, this also forms the core area for application of the package. Shoreline dating is based on the present-day elevation of a site, a reconstruction of past relative sea-level change, and empirically derived estimates of the likely elevation of the sites above the contemporaneous sea-level when they were in use. The geographical and temporal coverage of the method thus follows from the availability of local geological reconstructions of shoreline displacement and the degree to which the settlements to be dated have been located on or close to the shoreline when they were in use. Methods for numerical treatment and visualisation of the dates are provided, along with basic tools for visualising and evaluating the location of sites.

Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.

Diagnostics for fixed effects linear and general linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202). Variance inflation factors and condition indexes are also computed for some general linear models as described in Liao (U. Maryland thesis, 2010).

This package provides a simulation-based tool made to help researchers to become familiar with multilevel variations, and to build up sampling designs for their study. This tool has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand distinct biological phenomena by interactively exploring simulated multilevel data. Second, it offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of data sets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) <doi: 10.1111/2041-210X.12659>].

Computes Strongest Neighbor Coherence (SNC), a structural diagnostic that replaces Cronbach's alpha using top-k correlation structure. For methodology, see Wells (2025) <https://github.com/TheotherDrWells/snc>.

Structural handling of identity numbers used in the Swedish administration such as personal identity numbers ('personnummer') and organizational identity numbers ('organisationsnummer').

This package provides a framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the Slick App. For more details, see the `Slick` website <https://slick.bluematterscience.com>.

Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.

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>.

Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).

This package provides a time input widget for Shiny. This widget allows intuitive time input in the [hh]:[mm]:[ss] or [hh]:[mm] (24H) format by using a separate numeric input for each time component. The interface with R uses date-time objects. See the project page for more information and examples.

This package provides functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) <doi:10.18637/jss.v066.i06>.

The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015 <doi:10.1162/NECO_a_00708>.

Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.

Pull data from the STAT Search Analytics API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.

In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.

This is a compendium of C++ routines useful for Bayesian statistics. We steal other people's C++ code, repurpose it, and export it so developers of R packages can use it in their C++ code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code.

Work with and download road traffic casualty data from Great Britain. Enables access to the UK's official road safety statistics, STATS19'. Enables users to specify a download directory for the data, which can be set permanently by adding `STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir` to your `.Renviron` file, which can be opened with `usethis::edit_r_environ()`. The data is provided as a series of `.csv` files. This package downloads, reads-in and formats the data, making it suitable for analysis. See the stats19 vignette for details. Data available from 1979 to 2024. See the official data series at <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data>. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) <doi:10.21105/joss.01181>. See Gilardi et al. (2022) <doi:10.1111/rssa.12823>, Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and León et al. (2025) <doi:10.18637/jss.v114.i09> for examples of how the data can be used for methodological and empirical research.

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.

Interface to sigma.js graph visualization library including animations, plugins and shiny proxies.

Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.

Programs to find the sample size or power of studies using the Sequential Parallel Comparison Design (SPCD) and programs to analyze such studies. This is a clinical trial design where patients initially on placebo who did not respond are re-randomized between placebo and active drug in a second phase and the results of the two phases are pooled. The method of analyzing binary data with this design is described in Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the method of analyzing continuous data is described in Chen, Yang, Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.

Network sparsification with a variety of novel and known network sparsification techniques. All network sparsification techniques reduce the number of edges, not the number of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. This package is based on the work of Spielman, D., Srivastava, N. (2009)<arXiv:0803.0929>. Koutis I., Levin, A., Peng, R. (2013)<arXiv:1209.5821>. Toivonen, H., Mahler, S., Zhou, F. (2010)<doi:10.1007>. Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.

An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).