This package provides tools for downloading and processing microdata from the PNAD Contà nua (PNADC, Continuous National Household Sample Survey), a rotating panel survey published quarterly by IBGE (Brazilian Institute of Geography and Statistics). Includes panel identification algorithms for linking individuals across survey waves.

This package contains functions for the DivE estimator <doi:10.1371/journal.pcbi.1003646>. The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.

This package provides a collection of functions to search and download DSM (Digital Surface Model) and LiDAR (Light Detection and Ranging) data via APIs, including OpenTopography <https://portal.opentopography.org/apidocs/> and TNMAccess <https://apps.nationalmap.gov/tnmaccess/#/>.

This package provides a collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see <https://www.ddaproject.com>.

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).

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

Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.

This package implements the DANDELION method for prioritizing candidate disease-related proximal genes by integrating trans-regulatory association p-values and gene-level trait association p-values. The statistical testing framework builds on the divide-aggregate composite-null test described by Liu et al. (2022) <doi:10.1080/01621459.2021.1914634>. The biological application and SNP/gene-based prioritization workflow are motivated by Salamone et al. (under review), "Leveraging trans-gene regulation prioritizes central genes and pathways in asthma". The package provides functions for identifying distal-proximal gene pairs, organizing significant pairs into genomic loci, and visualizing resulting gene networks.

Several functions are provided for dose-response (or concentration-response) characterization from omics data. DRomics is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). DRomics provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, DRomics also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.

This package provides tools for working with a new versatile discrete distribution, the db ("discretised Beta") distribution. This package provides density (probability), distribution, inverse distribution (quantile) and random data generation functions for the db family. It provides functions to effect conveniently maximum likelihood estimation of parameters, and a variety of useful plotting functions. It provides goodness of fit tests and functions to calculate the Fisher information, different estimates of the hessian of the log likelihood and Monte Carlo estimation of the covariance matrix of the maximum likelihood parameter estimates. In addition it provides analogous tools for working with the beta-binomial distribution which has been proposed as a competitor to the db distribution.

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

This package provides friendly wrappers for creating duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the arrow package, but in addition to S3 file system also generalizes to any list of http URLs.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.

Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.

This package provides methods for testing the equality between groups of estimated density functions. The package implements FDET (Fourier-based Density Equality Testing) and MDET (Moment-based Density Equality Testing), two new approaches introduced by the author. Both methods extend an earlier testing approach by Delicado (2007), "Functional k-sample problem when data are density functions" <doi:10.1007/s00180-007-0047-y>, which is referred to as DET (Density Equality Testing) in this package for clarity. FDET compares groups of densities based on their global shape using Fourier transforms, while MDET tests for differences in distributional moments. All methods are described in Anarat, Krutmann and Schwender (2025), "Testing for Differences in Extrinsic Skin Aging Based on Density Functions" (Submitted).

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.

Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) ReliefWeb disaster events at <https://reliefweb.int/disasters>. The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow.

This package provides methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465â 476, <doi:10.1111/biom.13337>.

Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data. DCPO uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with Stan'. References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.

This package provides tools to identify, quantify, analyze, and visualize growth suppression events in tree rings that are often produced by insect defoliation. Described in Guiterman et al. (2020) <doi:10.1016/j.dendro.2020.125750>.

Dominance analysis relative importance methods that are intended for predictive models.

Providing six different algorithms that can be used to split the available data into training, test and validation subsets with similar distribution for hydrological model developments. The dataSplit() function will help you divide the data according to specific requirements, and you can refer to the par.default() function to set the parameters for data splitting. The getAUC() function will help you measure the similarity of distribution features between the data subsets. For more information about the data splitting algorithms, please refer to: Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>, Zheng et al. (2022) <doi:10.1029/2021WR031818>.

Smooth testing of goodness of fit. These tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find various tests for exponent, Gaussian, Gumbel and uniform distribution.

This package provides a wrapper for Google's diff-match-patch library. It provides basic tools for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.