Get information about air quality using Airly <https://airly.eu/> API through R.

Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).

An efficient Rcpp implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.

Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) <DOI:10.1017/nws.2013.17>. Development of this software was supported in part by NIH grant R01HD067509.

Flagger to detect acute kidney injury (AKI) in a patient dataset.

In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide aPCoA as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101.

This package provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmottâ s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Allows the user to implement an address search auto completion menu on shiny text inputs. This is done using the Algolia Places JavaScript library. See <https://community.algolia.com/places/>.

This package provides a set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the admiral package.

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.

Accurate point and interval estimation methods for multiple linear regression coefficients, under classical normal and independent error assumptions, taking into account variable selection.

This package provides novel nonparametric tests, APCSSA and APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package APCinteractionData is available on GitHub <https://github.com/tranbaokhue/APCinteractionData>. Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) <doi:10.48550/arXiv.2410.04700>.

Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers.

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>, and Tong Lin, Jinseok Lee, Matt Helgeson, Megan T Valentine, Yimin Luo, Mengyang Gu (2026), <doi:10.48550/arXiv.2605.29424>.

Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The ashapesampler package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.

This package provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the asteRiskData package, available in a drat repository. To install this data package, run install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>. David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>. Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.

This package provides functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators.

An R wrapper for agena.ai <https://www.agena.ai> which provides users capabilities to work with agena.ai using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to agena.ai cloud or local API for calculations. Note: running calculations requires a valid agena.ai API license (past the initial trial period of the local API).

Simple and transparent parsing of genotype/dosage data from an input Variant Call Format (VCF) file, matching of genotype coordinates to the component Single Nucleotide Polymorphisms (SNPs) of an existing polygenic score (PGS), and application of SNP weights to dosages for the calculation of a polygenic score for each individual in accordance with the additive weighted sum of dosages model. Methods are designed in reference to best practices described by Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.

This package implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

This package provides an automatic aggregation tool to manage point data privacy, intended to be helpful for the production of official spatial data and for researchers. The package pursues the data accuracy at the smallest possible areas preventing individual information disclosure. The methodology, based on hierarchical geographic data structures performs aggregation and local suppression of point data to ensure privacy as described in Lagonigro, R., Oller, R., Martori J.C. (2017) <doi:10.2436/20.8080.02.55>. The data structures are created following the guidelines for grid datasets from the European Forum for Geography and Statistics.

Extract and process bird sightings records from eBird (<http://ebird.org>), an online tool for recording bird observations. Public access to the full eBird database is via the eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable text file. This package is an interface to AWK for extracting data from the EBD based on taxonomic, spatial, or temporal filters, to produce a manageable file size that can be imported into R.

This package provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.