This package provides a sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate uncertainty about the sensitivity parameters and sampling variability. Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference. For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.

Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.

Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) <doi:10.1080/10705511.2017.1373021>, penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, penalized regression p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, as well as traditional p-values such as Satorra-Bentler. All p-values can be calculated using unbiased or biased gamma estimates (Du, Bentler, 2022) <doi:10.1080/10705511.2022.2063870> and two choices of chi square statistics.

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>).

The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) <doi:10.2307/2332748>. When s<t, SPAS implements the methods described in Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>. Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name.

Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.

Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the OpenAPI (a.k.a., swagger') specification, and all return values are checked in order to make them type stable.

This package provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.

This package implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest.

This package provides functions to speed up the exploratory analysis of simple datasets using dplyr'. Functions are provided to do the common tasks of calculating confidence intervals.

Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.

This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about SlideCNA is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package enrichR (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").

It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

By adding dependencies to the "Suggests" field of a package's DESCRIPTION file, and then declaring that they are needed within any dependent functionality, it is often possible to significantly reduce the number of "hard" dependencies required by a package. This package provides a minimal way to declare when a suggested package is needed.

This package provides tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

This package provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).

Fit a spatial-temporal occupancy models using a probit formulation instead of a traditional logit model.

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

This package implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm PatchMorph (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on lidR (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.

This package provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.

This package provides functionality for analytically calculating parameters (via the InteractionPoweR package) useful for simulation of moderated multiple regression, based on the correlations among the predictors and outcome and the reliability of predictors.

Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using lme4 syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).

This package provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.