Miscellaneous functions for working with stars objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.

Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.

Sequential triangular test for the arithmetic mean in one- and two- samples, proportions in one- and two-samples, and the Pearson's correlation coefficient.

An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>. It provides a novel anomaly detection method for multivariate noisy sensor data. It can automatically handle multiple operational modes. And it can also compute variable-wise anomaly scores.

This package provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage C++ for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

This package provides an interface to the Sensibo Sky API which allows to remotely control non-smart air conditioning units. See <https://sensibo.com> for more informations.

This package provides functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., bootnet package.

Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

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.

Inference techniques for Fay Herriot Model.

This package provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including CSV', Excel', RIS', BibTeX', and EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to PubMed for direct database searching and real-time strategy comparison using the E-utilities API'. Enhanced with bootstrap comparison methods, McNemar test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for PRISMA guidelines, and Sampson et al. (2006) for systematic review search methodology.

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.

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) <doi:10.1103/PhysRevE.88.062819>. The high speed and the low memory usage make it suitable for large graphs.

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The splithalfr supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals.

Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). SemanticDistance computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. SemanticDistance can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). SemanticDistance includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network).

This package provides a series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis.

Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. SpatFD is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892â 894.

Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.

Alternative to using withCallingHandlers() in the simple case of catch and rethrow. The `%!%` operator evaluates the expression on its left hand side, and if an error occurs, the right hand side is used to construct a new error that embeds the original error.

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.

This package implements S3 classes for storing dates and date-times based on the Jalali calendar. The main design goal of shide is consistency with base R's Date and POSIXct'. It provide features such as: date-time parsing, formatting and arithmetic.