Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.

If f <- function(x)x^2 and g <- function(x)x+1 it is a constant source of annoyance that "f+g" is not defined. Package vfunc allows you to do this, and we have (f+g)(2) returning 5. The other arithmetic operators are similarly implemented. A wide class of coding bugs is eliminated.

This package performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.

Response surface designs with neighbour effects are suitable for experimental situations where it is expected that the treatment combination administered to one experimental unit may affect the response on neighboring units as well as the response on the unit to which it is applied (Dalal et al.,2025 <doi: 10.57805/revstat.v23i2.513>). Integrating these effects in the response surface model improves the experiment's precision Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)<doi: 10.1080/03610918.2021.1890123>). This package includes sym(), asym1(), asym2(), asym3() and asym4() functions that generates response surface designs which are rotatable under a polynomial model of a given order without interaction term incorporating neighbour effects.

This library implements the RSA encryption and signature algorithms for arbitrarily-sized ByteStrings. While the implementations work, they are not necessarily the fastest ones on the planet. Particularly key generation. The algorithms included are based of RFC 3447, or the Public-Key Cryptography Standard for RSA, version 2.1 (a.k.a, PKCS#1 v2.1).

GNU Rot[t]log is a program for managing log files. It is used to automatically rotate out log files when they have reached a given size or according to a given schedule. It can also be used to automatically compress and archive such logs. Rot[t]log will mail reports of its activity to the system administrator.

Machine Learning models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack interpretability. The DALEX package contains various explainers that help to understand the link between input variables and model output.

Ggdag is built on top of dagitty, an R package that uses the DAGitty web tool for creating and analyzing DAGs. ggdag makes it easy to tidy and plot dagitty objects using ggplot2 and ggraph, as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

This package provides tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bezier lines with arrows complementing the ones in the grid package, and more.

This package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

This library implements the RSA encryption and signature algorithms for arbitrarily-sized ByteStrings. While the implementations work, they are not necessarily the fastest ones on the planet. Particularly key generation. The algorithms included are based of RFC 3447, or the Public-Key Cryptography Standard for RSA, version 2.1 (a.k.a, PKCS#1 v2.1).

Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.

The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis.

PLAID (Pathway Level Average Intensity Detection) is an ultra-fast method to compute single-sample enrichment scores for gene expression or proteomics data. For each sample, plaid computes the gene set score as the average intensity of the genes/proteins in the gene set. The output is a gene set score matrix suitable for further analyses.

The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.

The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.".

This package provides some easy-to-use functions and classes to calculate variability measures such as coefficient of variation with confidence intervals provided with all available methods. References are Panichkitkosolkul (2013) <doi:10.1155/2013/324940>, Altunkaynak & Gamgam (2018) <doi:10.1080/03610918.2018.1435800>, Albatineh', Kibria', Wilcox & Zogheib (2014) <doi:10.1080/02664763.2013.847405>.

Este pacote traduz os seguintes conjuntos de dados: airlines', airports', ames_raw', AwardsManagers', babynames', Batting', diamonds', faithful', fueleconomy', Fielding', flights', gapminder', gss_cat', iris', Managers', mpg', mtcars', atmos', penguins', People, Pitching', pixarfilms','planes', presidential', table1', table2', table3', table4a', table4b', table5', vehicles', weather', who'. English: It provides a Portuguese translated version of the datasets listed above.

This package provides functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the msm package using age as the time-scale.

Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.

Analysis of Bayesian adaptive enrichment clinical trial using Free-Knot Bayesian Model Averaging (FK-BMA) method of Maleyeff et al. (2024) for Gaussian data. Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" <doi:10.1093/biomtc/ujae141>.

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

This package provides optimized C++ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via OpenMP was implemented with assistance from the DeepSeek Artificial Intelligence Assistant (<https://www.deepseek.com/>).

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.