This wrapper package for mgcv makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, AutoGAM tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

This package provides a web framework inspired by express.js to build any web service from multi-page websites to RESTful application programming interfaces.

Manage author metadata and generate manuscript front matter. It produces title pages, acknowledgements, conflicts of interest, and contribution sections for large author lists, with helpers for validating and reading common spreadsheet formats.

Power and associated functions useful in prospective planning and monitoring of a clinical trial when a recurrent event endpoint is to be assessed by the robust Andersen-Gill model, see Lin, Wei, Yang, and Ying (2010) <doi:10.1111/1467-9868.00259>. The equations developed in Ingel and Jahn-Eimermacher (2014) <doi:10.1002/bimj.201300090> and their consequences are employed.

The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022).

Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) <doi:10.1002/sim.4780020223>, and Muse et al., (2022) <doi:10.1016/j.aej.2022.01.033>.

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

This package provides a simple interface to the instance metadata for a virtual machine running in Microsoft's Azure cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the AzureR family of packages.

Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (<https://incept.dk/>), primarily supported by a grant from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).

This package provides tools for detecting, quantifying, and visualizing algorithmic bias as a longitudinal process in repeated decision systems. Existing fairness metrics treat bias as a single-period snapshot; this package operationalizes the view that bias in sequential systems must be measured over time. Implements group-specific decision-rate trajectories, standardized disparity measures analogous to the standardized mean difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative bias burden, Markov-based transition disparity (recovery and retention gaps), and a dynamic amplification index that quantifies whether prior decisions compound current group inequality. The amplification framework extends longitudinal causal inference ideas from Robins (1986) <doi:10.1016/0270-0255(86)90088-6> and the sequential decision-process perspective in the fairness literature (see <https://fairmlbook.org>) to the audit setting. Covariate-adjusted trajectories are estimated via logistic regression, generalized additive models (Wood, 2017, <doi:10.1201/9781315370279>), or generalized linear mixed models (Bates, 2015, <doi:10.18637/jss.v067.i01>). Uncertainty quantification uses the cluster bootstrap (Cameron, 2008, <doi:10.1162/rest.90.3.414>).

Retrieve air quality data via the AirNow <https://www.airnow.gov/> API.

Uses Auth0 API (see <https://auth0.com> for more information) to use a simple authentication system. It provides tools to log in and out a shiny application using social networks or a list of e-mails.

This package provides a collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.

Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them.

Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians. The package also provides helper functions for loading an insurance scoring dataset, a past case competition dataset for insurance risk scoring and fair pricing.

For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) <DOI:10.1111/rssc.12558>.

This package provides a Shiny application to access the functionalities and datasets of the archeofrag package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the openMOLE model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

The process of resolving and updating taxon names is necessary when working with biodiversity data. APCalign uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. APCalign also supplies information about the establishment status (i.e. native or introduced) of plant taxa across different states/territories.

This package provides a tool for generating acronyms and initialisms from arbitrary text input.

This package provides a few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.

Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.

Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/> and Rob J Hyndman et al "Documentation for forecast'" (2003) <https://pkg.robjhyndman.com/forecast/>, and ggplot2', described in Hadley Wickham et al "Documentation for ggplot2'" (2015) <https://ggplot2.tidyverse.org/>, and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" <https://ggplot2-book.org/>.

Generate spreadsheet publications that follow best practice guidance from the UK government's Analysis Function, available at <https://analysisfunction.civilservice.gov.uk/policy-store/releasing-statistics-in-spreadsheets/>, with a focus on accessibility. See also the Python package gptables'.

This package provides a tidy text corpus of Aesop's Fables sourced from the Library of Congress, along with analysis-ready datasets for sentiment, emotion, and linguistic analysis of moral storytelling. The package includes both full narrative texts and word-level representations to support exploratory text analysis and teaching workflows.