Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

Record asciicast screen casts from R scripts. Convert them to animated SVG images, to be used in README files, or blog posts. Includes asciinema-player as an HTML widget, and an asciicast knitr engine, to embed ascii screen casts in Rmarkdown documents.

We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model.

This package implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.

An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. Estimators implemented are the BCML (Elhorst (2010), <doi:10.1016/j.regsciurbeco.2010.03.003>), the MML (Elhorst (2010) <doi:10.1016/j.regsciurbeco.2010.03.003>) and the INLA Bayesian estimator (Lindgren and Rue, (2015) <doi:10.18637/jss.v063.i19>; Bivand, Gomez-Rubio and Rue, (2015) <doi:10.18637/jss.v063.i20>) adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), <doi:10.1111/ajae.12155>)).

Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" <doi:10.1111/faf.12154>.

This package provides a summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF).

This package provides a user-friendly shiny application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.

Allows access to selected services that are part of the Google Adwords API <https://developers.google.com/adwords/api/docs/guides/start>. Google Adwords is an online advertising service by Google', that delivers Ads to users. This package offers a authentication process using OAUTH2'. Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses SOAP requests which require building an XML structure and then sent to the API. These are used for the ManagedCustomerService and the TargetingIdeaService'. The second method is by building AWQL queries for the reporting side of the Google Adwords API.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

This package provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <doi:10.48550/arXiv.1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <doi:10.48550/arXiv.2004.12655>.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713).

This package provides sleep duration estimates using a Pruned Dynamic Programming (PDP) algorithm that efficiently identifies change-points. PDP applied to physical activity data can identify transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker, Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford, E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data <doi:10.1007/s12561-021-09309-3>.

Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).

This package provides a simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed.

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>.

This package provides a set of functions for interacting with the DigitalOcean API <https://www.digitalocean.com/>, including creating images, destroying them, rebooting, getting details on regions, and available images.

Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package.

Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at <https://en.wikipedia.org/wiki/Shoelace_formula>.

The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.

This package provides an interface in R to cell atlas approximations. See the vignette under "Getting started" for instructions. You can also explore the reference documentation for specific functions. Additional interfaces and resources are available at <https://atlasapprox.readthedocs.io>.

Analysis of moderation (ANOMO) method conceptualizes the difference and equivalence tests as a moderation problem to test the difference and equivalence of two estimates (e.g., two means or two effects).