Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.

Check if a given package name is available to use. It checks the name's validity. Checks if it is used on GitHub', CRAN and Bioconductor'. Checks for unintended meanings by querying Wiktionary and Wikipedia.

Facilitates access to the data from the Atlas do Estado Brasileiro (<https://www.ipea.gov.br/atlasestado/>), maintained by the Instituto de Pesquisa Econômica Aplicada (Ipea). It allows users to search for specific series, list series or themes, and download data when available.

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the pamr <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the pamr algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.

An interface to the API for arXiv', a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics.

This package provides tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated.

Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community <http://docs.airr-community.org>.

This package provides functions to create image annotations through polygon outlining. Annotator has the same function as graphics::locator() but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the htmlwidgets package and fabric.js JavaScript library <https://fabricjs.com/>.

This package provides a collection of measures for measuring ecological diversity. Ecological diversity comes in two flavors: alpha diversity measures the diversity within a single site or sample, and beta diversity measures the diversity across two sites or samples. This package overlaps considerably with other R packages such as vegan', gUniFrac', betapart', and fossil'. We also include a wide range of functions that are implemented in software outside the R ecosystem, such as scipy', Mothur', and scikit-bio'. The implementations here are designed to be basic and clear to the reader.

Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.

Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).

Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.

Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.

Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

Functionality to allow users to easily colour plots with the colour palettes of various academic institutions.

This package implements a simple version of multivariate matching using a propensity score, near-exact matching, near-fine balance, and robust Mahalanobis distance matching (Rosenbaum 2020 <doi:10.1146/annurev-statistics-031219-041058>). You specify the variables, and the program does everything else.

This package provides a collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar.

This package provides a thin wrapper around the ajv JSON validation package for JavaScript. See <http://epoberezkin.github.io/ajv/> for details.

This package provides a Python based pipeline for extraction of species occurrence data through the usage of large language models. Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) <doi:10.1002/asi.5090060209>, van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) <https://nymity.ch/sybilhunting/pdf/Levenshtein1966a.pdf> and Klaus Krippendorff (2011) <https://repository.upenn.edu/handle/20.500.14332/2089>.

This package contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025a) <doi:10.1080/09332480.2025.2473291>. Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs.

This package implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) <doi:10.1093/biomet/90.2.341>), Hellerâ s estimating method (Heller (2012) <doi:10.1198/016214506000001257>), Polynomial smoothed Gehan function method (Chung (2013) <doi:10.1007/s11222-012-9333-9>), Buckley-James method (Buckley (1979) <doi:10.2307/2335161>) and Jin`s improved least squares method (Jin (2006) <doi:10.1093/biomet/93.1.147>). This package can be used for modeling right-censored data and for comparing different estimation algorithms.

Implementation of the technique of Lleonart et al. (2000) <doi:10.1006/jtbi.2000.2043> to scale body measurements that exhibit an allometric growth. This procedure is a theoretical generalization of the technique used by Thorpe (1975) <doi:10.1111/j.1095-8312.1975.tb00732.x> and Thorpe (1976) <doi:10.1111/j.1469-185X.1976.tb01063.x>.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).