Generalized additive models under shape constraints on the component functions of the linear predictor. Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package mgcv are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.

Implementation of the BLEU-Score in C++ to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.

The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.

This package provides a collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling, introduced in Yang and Cheung (2026) <doi: 10.3758/s13428-025-02911-z> The function currently support models fitted by the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>.

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.

This package provides developer-focused helper functions and S3 classes to ease the creation of structured subtypes of data frames. Developers can require certain columns and types to be present, and can enforce crossing and nesting relationships between values in different columns. Type-specific metadata and attributes are preserved through common data frame manipulations.

In population management, data come at more or less regular intervals over time in sampling batches (bouts) and decisions should be made with the minimum number of samples and as quickly as possible. This package provides tools to implement, produce charts with stop lines, summarize results and assess sequential analyses that test hypotheses about population sizes. Two approaches are included: the sequential test of Bayesian posterior probabilities (Rincon, D.F. et al. 2025 <doi:10.1111/2041-210X.70053>), and the sequential probability ratio test (Wald, A. 1945 <http://www.jstor.org/stable/2235829>).

Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures.

This package contains several tools for nonlinear regression analyses and general data analysis in biology and agriculture. Contains also datasets for practicing and teaching purposes. Supports the blog: Onofri (2024) "Fixing the bridge between biologists and statisticians" <https://www.statforbiology.com> and the book: Onofri (2024) "Experimental Methods in Agriculture" <https://www.statforbiology.com/_statbookeng/>. The blog is a collection of short articles aimed at improving the efficiency of communication between biologists and statisticians, as pointed out in Kozak (2016) <doi:10.1590/0103-9016-2015-0399>, spreading a better awareness of the potential usefulness, beauty and limitations of biostatistic.

Shiny wrappers for the RGL package. This package exposes RGL's ability to export WebGL visualization in a shiny-friendly format.

This package provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2> methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only.

This package provides a set of functions for querying and parsing data from Solr (<https://solr.apache.org/>) endpoints (local and remote), including search, faceting', highlighting', stats', and more like this'. In addition, some functionality is included for creating, deleting, and updating documents in a Solr database'.

Run SQL queries across Snowflake', Amazon Redshift', PostgreSQL', SQLite', and DuckDB from R with a single function. Optionally stream and cache large query results to a local DuckDB database for efficient work with larger-than-memory datasets.

This package provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.

This package provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.

This package provides a compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.

Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the OpenAPI (a.k.a., swagger') specification, and all return values are checked in order to make them type stable.

Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.

This package provides a collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web server space has been provided by Ricardo Energy & Environment.

Simple classic graph algorithms for simple graph classes. Graphs may possess vertex and edge attributes. simplegraph has no dependencies and it is written entirely in R, so it is easy to install.

This package provides methods and data for cluster detection and disease mapping.

This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at <https://surveydefense.org>.

This package implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in C++ using RcppArmadillo and integrated into R via Rcpp modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.

Population genetics package for designing diagnostic panels. Candidate markers, marker combinations, and different panel sizes are assessed for how well they can predict the source population of known samples. Requires a genotype file of candidate markers in STRUCTURE format. Methods for population cross-validation are described in Jombart (2008) <doi:10.1093/bioinformatics/btn129>.