Probabilistic Streaming Data Sketches

Provides an interface to the 'Apache DataSketches' (< https://datasketches.apache.org/>) library of streaming algorithms for approximate analytics on data too large to hold or process exactly. Sketches are compact, mergeable summaries built in a single pass over a stream that answer queries such as approximate distinct counts, quantiles and ranks, frequent items and point-frequency estimates, weighted sampling, and set membership with mathematically proven error bounds. Implements Karnin-Lang-Liberty (KLL), Relative Error Quantiles (REQ), t-Digest, HyperLogLog (HLL), Compressed Probabilistic Counting (CPC), Theta, Frequent Items, Count-Min, Array of Doubles, Variance Optimal (VarOpt), Exact and Bounded Probabilistic Proportional-to-Size (EBPPS), and Bloom filter sketches, with native serialization for interoperability with other 'Apache DataSketches' implementations.

Heteroskedasticity-Consistent Inference for Linear Models

Computes heteroskedasticity-consistent covariance matrix estimators for ordinary least squares regression models. The published HC0 through HC5m estimators implemented in the package follow White (1980) , Hinkley (1977) , Horn et al. (1975) , MacKinnon and White (1985) , Cribari-Neto (2004) , Cribari-Neto and da Silva (2011) , Cribari-Neto et al. (2007) , and Li et al. (2016) . The package also includes HCbeta, a new estimator proposed by the package authors. It provides normal Wald tests, confidence intervals, diagnostics, and S3 output for applied inference.

Classification of Algorithms

Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions . Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values .

Spatial Pattern Detection in Genetic Distance Data Using Moran's Eigenvector Maps

Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation. Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.

Item Analysis in Rasch Models

Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).

Treatment Effects with Multiple Periods and Groups

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) . The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

Simulation Tool for Causal Inference Using Longitudinal Data

Implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) and Griffin et al. (2022) for a description of our methods.

Triple-Difference Estimators

Implements triple-difference (DDD) estimators for both average treatment effects and event-study parameters. Methods include regression adjustment, inverse-probability weighting, and doubly-robust estimators, all of which rely on a conditional DDD parallel-trends assumption and allow covariate adjustment across multiple pre- and post-treatment periods. The methodology is detailed in Ortiz-Villavicencio and Sant'Anna (2025) .

Spatial Prediction for Function Value Data

Kriging based methods are used for predicting functional data (curves) with spatial dependence.

Langevin Analysis in One and Two Dimensions

Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.