The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weighted) score is calculated based on the type of food. This package is intended to calculate the DDP/ PPH score that is faster than traditional method via a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey <http://www.nutrisurvey.de>. The database to create weights and baseline values is the Indonesia national survey in 2017.

Model-based methods for the detection of disease clusters using GLMs, GLMMs and zero-inflated models. These methods are described in V. Gómez-Rubio et al. (2019) <doi:10.18637/jss.v090.i14> and V. Gómez-Rubio et al. (2018) <doi:10.1007/978-3-030-01584-8_1>.

Estimation of heterogeneity-robust difference-in-differences estimators, with a binary, discrete, or continuous treatment, in designs where past treatments may affect the current outcome.

This package provides a comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for Cutadapt and DADA2 to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into phyloseq and taxmap objects. For more information on DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) <doi:10.1038/nmeth.3869>. For more details on the demulticoder R package see Sudermann et al. (2025) <doi:10.1094/PHYTO-02-25-0043-FI>.

This package provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds" in <doi:10.1098/rsif.2019.0234>. Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis.

Streamlines analysis of qualitative data exported from Dedoose <https://www.dedoose.com>. Supports monitoring thematic saturation, calculating code frequencies, organizing excerpts, generating dynamic codebooks, and producing code network maps within R'.

Generate motivational quotes and Shakespearean word combinations (bardâ bits) that a user can consider for their personal projects. Each of the package functions takes two arguments, cat which default to any, and a a numeric or character seed to ensure reproducible results.

Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.

This package provides functions to download, process, and visualize German geospatial data across administrative levels, including states, districts, and municipalities. Supports interactive tables and customized maps using built-in or external datasets. Official shapefiles are accessed from the German Federal Agency for Cartography and Geodesy (BKG) <https://gdz.bkg.bund.de/>, licensed under dl-de/by-2-0 <https://www.govdata.de/dl-de/by-2-0>.

Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension. The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.

Work with data on Venetian doges and dogaresse and the noble families of the Republic of Venice, and use it for social network analysis, as used in Merelo (2022) <doi:10.48550/arXiv.2209.07334>.

This package performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.

Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions.

Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.

Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.

The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, <doi: 10.1016/j.xplc.2022.100319>).

Overload utils::'? to build unary and binary operators from existing functions, piping operators of different precedence, and flexible syntaxes.

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.

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the bnlearn package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the visNetwork package. Further detailed information and examples can be found in our Journal of Statistical Software paper Quesada D., Larrañaga P. and Bielza C. (2025) <doi:10.18637/jss.v115.i06>.

This package provides a set of pricing and expository functions that should be useful in teaching a course on financial derivatives.

Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.

An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) <doi:10.1093/genetics/iyac126>.

Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review).

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.