Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes LatticeKrig package.

This package provides a function for estimating factor models. Give factor-adjusted statistics, factor-adjusted mean estimation (one-sample test) or factor-adjusted mean difference estimation (two-sample test).

This package provides functions for the analysis of ant communities, aiming to standardize workflows in myrmecology. The package automates the assignment of species to functional guilds based on trophic strategies, feeding habits, and foraging behavior, using established classification frameworks (Silva et al., 2015 <doi:10.7476/9788574554419>; Silvestre et al., 2003 <isbn:9588151236>; Delabie et al., 2000 <https://www.researchgate.net/publication/44961742_Sampling_Ground-Dwelling_Ants_Case_Studies_from_the_World%27s_Rain_Forests>), and also includes a novel classification system implemented within the package, developed from ant species occurring in urban environments. It also includes routines to flag exotic species of Brazil (Vieira, 2025, unpublished master's thesis), identify endemic species (Silva et al., 2025 <doi:10.37885/250920259>), and classify species rarity and rarity forms of the Atlantic Forest (Silva et al., 2024 <doi:10.1016/j.biocon.2024.110640>). The package reduces manual effort and improves reproducibility, supporting research and biodiversity management of Neotropical ant communities.

Argument parsing for R scripts, with support for long and short Unix-style options including option clustering, positional arguments including those of variable length, and multiple usage patterns which may take different subsets of options.

This package provides tools for simulating, monitoring, and evaluating Bayesian and adaptive clinical trial designs with binary outcomes. The package includes methods for trial simulation, posterior and predictive probability monitoring, adaptive randomization, Bayesian treatment-effect estimation, dose-finding designs, two-stage phase II trial designs, group sequential monitoring, operating-characteristic evaluation, interim analyses, and decision-support procedures. The methodological foundation includes the two-stage phase II design described by Simon (1989) <doi:10.1016/0197-2456(89)90015-9>.

This package provides a simple interface to the Microsoft Graph API <https://learn.microsoft.com/en-us/graph/overview>. Graph is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the Azure Active Directory part, with a view to supporting interoperability of R and Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the AzureR family of packages.

This package implements the double-sampling bounds estimator of Coppock, Gerber, Green, and Kern (2017) <doi:10.1017/pan.2016.6> for randomized experiments with nonignorable missing outcomes. Provides worst-case (Manski) bounds, double-sampling bounds with analytic variance and Imbens-Manski confidence intervals, Lee (2009) <doi:10.1111/j.1467-937X.2009.00536.x> trimming bounds with analytic and bootstrap standard errors, covariate adjustment via poststratification, and a sensitivity analysis for violations of the outcome stability assumption.

This package provides a collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.

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.

This package provides functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) <doi: 10.1016/j.cmpb.2007.07.010> and Zhang et al., (2011) <doi: 10.1016/j.cmpb.2010.07.005> respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

This package implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the arules package. To further decrease the size of the CBA models produced by the arc package, postprocessing by the qCBA package is suggested.

Convert several png files into an animated png file. This package exports only a single function `apng'. Call the apng function with a vector of file names (which should be png files) to convert them to a single animated png file.

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/>.

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

This package provides an integrated pipeline for content analysis combining qualitative coding assisted by large language models (LLMs) with classical quantitative text analysis. Includes modules for pre-processing (tokenization, stopwords for Brazilian Portuguese), descriptive statistics, keyness, co-occurrence networks, word clouds (including comparative and X-ray variants), sentiment analysis via OpLexicon, Latent Dirichlet Allocation (LDA), inter-coder reliability metrics (Krippendorff, Gwet), and modern visualizations based on ggplot2. Special focus on Brazilian corpora and political-institutional codebooks. Inspired by Maerz and Benoit (2025) <https://quallmer.github.io/quallmer/>.

This package provides R bindings to the Automerge Conflict-free Replicated Data Type ('CRDT') library. Automerge enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) <doi:10.1145/3359591.3359737>. This package supports all Automerge data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with JavaScript and other Automerge implementations.

This package provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) <doi:10.1577/C08-025.1> for data-limited stock assessment and Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) <doi:10.1016/j.fishres.2019.105474> formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to Month and Length from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in day.month.year format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) <doi:10.1577/C08-025.1> and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting.

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from Seurat objects, for GRN analysis by ANANSE implemented in snakemake'. Finally, incorporate results for visualization and interpretation.

This package implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

Package to incorporate change point analysis in ARIMA forecasting.

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>.

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.

This package provides a simple client for the Amazon Web Services ('AWS') Identity and Access Management ('IAM') API <https://aws.amazon.com/iam/>.