Providing the functions for communicating with Amazon Web Services(AWS) Elastic Compute Cloud(EC2) and Elastic Container Service(ECS). The functions will have the prefix ecs_ or ec2_ depending on the class of the API. The request will be sent via the REST API and the parameters are given by the function argument. The credentials can be set via aws_set_credentials'. The EC2 documentation can be found at <https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Welcome.html> and ECS can be found at <https://docs.aws.amazon.com/AmazonECS/latest/APIReference/Welcome.html>.

Package for the access and distribution of long-term lake datasets from 28 lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters originally described in Farrell et al. 2018 <doi:10.1038/sdata.2018.59>. Water chemistry and nutrient records are extended through 2024 using data from the USGS AQ Samples database, including new columns for surface temperature, UV-254 absorbance, and a program flag distinguishing AEAP integrated samples from ALTM surface grabs. The underlying figshare archive <doi:10.6084/m9.figshare.32305479> additionally contains chemistry records for 25 ALTM-only lakes; the package restricts to the 28 originals for consistency with the published dataset.

This package provides a simple interface to the Microsoft Graph API <https://learn.microsoft.com/en-us/graph/overview>. Graph is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the Azure Active Directory part, with a view to supporting interoperability of R and Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the AzureR family of packages.

This package provides a collection of tools for the analysis of habitat selection.

This package provides methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

This package provides a collection of Japanese text processing tools for filling Japanese iteration marks, Japanese character type conversions, segmentation by phrase, and text normalization which is based on rules for the Sudachi morphological analyzer and the NEologd (Neologism dictionary for MeCab'). These features are specific to Japanese and are not implemented in ICU (International Components for Unicode).

Clinical trial design for subgroup selection in three-stage group sequential trial as described in Lai, Lavori and Liao (2014, <doi:10.1016/j.cct.2014.09.001>). Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette.

Tuning-free inference on fixed-dimensional parameters of dependent time series using affine-equivariant adjusted-range self-normalization. The centered partial-sum path of estimated influence contributions is normalized by its increment hull, the convex hull of all path increments. The gauge of the hull provides an asymptotically pivotal test statistic and an affine-equivariant confidence region without estimating the long-run covariance matrix, and its support function gives simultaneous confidence intervals for linear contrasts. For a single parameter the construction reduces exactly to adjusted-range self-normalization, whose limiting distribution is available in closed form. The Brownian reference law is simulated on a grid matched to the sample size or a supplied common variance-accumulation profile; inference for dependent observations remains asymptotic. Five further methods are provided for comparison on the same estimate and influence contributions: componentwise adjusted ranges after lag-zero partial prewhitening, quadratic self-normalization following Shao (2010) <doi:10.1111/j.1467-9868.2009.00737.x>, kernel long-run covariance estimation with automatic bandwidth selection following Andrews (1991) <doi:10.2307/2938229> and Newey and West (1994) <doi:10.2307/2297912>, Bartlett fixed-b inference following Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and the equal-weighted cosine method of Lazarus, Lewis, Stock and Watson (2018) <doi:10.1080/07350015.2018.1506926>. Model interfaces are provided for sample means, linear regression, smooth generalized method of moments, and conditional likelihood scores; other estimators are handled through user-supplied influence contributions. The methods follow Hong, Lin, Linton, Newey and Sun (2026), Cambridge Working Papers in Economics No. 2678 <https://www.janeway.econ.cam.ac.uk/publication/affine-equivariant-adjusted-range-self-normalization> and, for the scalar case, Hong, Linton, McCabe, Sun and Wang (2024) <doi:10.1016/j.jeconom.2023.105603>.

Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 <doi:10.1201/9781420010138>), extended regression calibration (Little et al. 2023 <doi:10.1038/s41598-023-42283-y>), Monte Carlo maximum likelihood (Stayner et al. 2007 <doi:10.1667/RR0677.1>), frequentist model averaging (Kwon et al. 2023 <doi:10.1371/journal.pone.0290498>), and Bayesian model averaging (Kwon et al. 2016 <doi:10.1002/sim.6635>). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. aggreCAT provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

This package implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>.

This package provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.

Collect your data on digital marketing campaigns from Apple Search Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides simple and intuitive functions for basic statistical analyses. Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>), the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>), Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>), and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>). Functions are compatible with ggplot2 and dplyr'.

This package implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) <doi:10.2139/ssrn.6618603> by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important.

This package implements a framework for creating boxplots where the whisker lengths are determined by formal multiple testing procedures, making them adaptive to sample size and data characteristics. The function bh_boxplot() generates boxplots that control the False Discovery Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. The methods are based on the framework in Gang, Lin, and Tong (2025) <doi:10.48550/arXiv.2510.20259>.

Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.

This package provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.

Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.

Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on GitHub'.

Create awesome Bootstrap 4 dashboards powered by Argon'.

This package implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression, classification and machine learning as introduced in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression and probit classification models, estimate the associated posterior distributions and make predictions. It is particularly useful for spatial data analysis, machine learning, complex function approximation and Bayesian modelling where the underlying structure is unknown.

This package implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) <doi:10.1093/biomet/90.2.341>), Hellerâ s estimating method (Heller (2012) <doi:10.1198/016214506000001257>), Polynomial smoothed Gehan function method (Chung (2013) <doi:10.1007/s11222-012-9333-9>), Buckley-James method (Buckley (1979) <doi:10.2307/2335161>) and Jin`s improved least squares method (Jin (2006) <doi:10.1093/biomet/93.1.147>). This package can be used for modeling right-censored data and for comparing different estimation algorithms.