This package provides select, insert, update, upsert, and delete database operations. Supports PostgreSQL', MySQL', SQLite', and more, and plays nicely with the DBI package.

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.

Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.

Parses command line arguments and supplies values to scripts. Users can specify names to which parsed inputs are assigned, value types into which inputs are cast, long options or short options, input splitters and callbacks that define how options should be specified and how input values are supplied.

Define a spatial Area of Interest (AOI) around a constructed dam using hydrology data. Dams have environmental and social impacts, both positive and negative. Current analyses of dams have no consistent way to specify at what spatial extent we should evaluate these impacts. damAOI implements methods to adjust reservoir polygons to match satellite-observed surface water areas, plot upstream and downstream rivers using elevation data and accumulated river flow, and draw buffers clipped by river basins around reservoirs and relevant rivers. This helps to consistently determine the areas which could be impacted by dam construction, facilitating comparative analysis and informed infrastructure investments.

This package provides a collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

This package provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.

Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.

This package provides tools to apply Ensemble Empirical Mode Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new algorithm, extricate, that performs EEMD in seconds, a linear interpolation algorithm using the greatest rational common divisor of depth or time, different algorithms to compute instantaneous amplitude, frequency and ratios of frequencies, and functions to verify and visualise the outputs. The functions were developed during the CRASH project (Checking the Reproducibility of Astrochronology in the Hauterivian). When using for publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, "A decomposition approach to cyclostratigraphic signal processing". Earth-Science Reviews 225 (103894). <doi:10.1016/j.earscirev.2021.103894>.

This package provides a collection of utility functions.

This package implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

This package provides a specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.

We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whippleâ s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate.

This package performs sensitivity analysis for the sharp null, attributable effects, and weak nulls in matched studies with continuous exposures and binary or continuous outcomes as described in Zhang, Small, Heng (2024) <doi:10.48550/arXiv.2401.06909> and Zhang, Heng (2024) <doi:10.48550/arXiv.2409.12848>. Two of the functions require installation of the Gurobi optimizer. Please see <https://docs.gurobi.com/current/#refman/ins_the_r_package.html> for guidance.

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

Builds interactive d3.js hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.

Visualizes variables from descriptive tables produced by descsuppR::buildDescrTbl() using ggstatsplot'. It automatically maps each variable to a suitable ggstatsplot plotting function based on the applied or suggested statistical test. Users can override the automatic mapping via a named list of plot specifications. The package supports grouped and ungrouped tables, and forwards additional arguments to the underlying ggstatsplot functions, providing quick, reproducible, and customizable default visualizations for descriptive summaries.

Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) <doi:10.3905/jpm.2006.611807>, Choueifaty and Coignard (2008) <doi:10.3905/JPM.2008.35.1.40>, Vermorken et al. (2012) <doi:10.3905/jpm.2012.39.1.067>, Flores et al. (2017) <doi:10.3905/jpm.2017.43.4.112>, Calvet et al. (2007) <doi:10.1086/524204>, and Candelon, Fuerst and Hasse (2020).

This package provides a set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.

This package provides a suite of tools are provided here to support authors in making their research more discoverable. check_keywords() - this function checks the keywords to assess whether they are already represented in the title and abstract. check_fields() - this function compares terminology used across the title, abstract and keywords to assess where terminological diversity (i.e. the use of synonyms) could increase the likelihood of the record being identified in a search. The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called litsearchr <https://github.com/elizagrames/litsearchr>.

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>, Vickers (2008) <doi:10.1186/1472-6947-8-53>, and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.

An R package for iterative and batched record linkage, and applying epidemiological case definitions. diyar can be used for deterministic and probabilistic record linkage, or multistage record linkage combining both approaches. It features the implementation of nested match criteria, and mechanisms to address missing data and conflicting matches during stepwise record linkage. Case definitions are implemented by assigning records to groups based on match criteria such as person or place, and overlapping time or duration of events e.g. sample collection dates or periods of hospital stays. Matching records are assigned a unique group ID. Index and duplicate records are removed or further analyses as required.