We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.

Conditional logistic regression with longitudinal follow up and individual-level random coefficients: A stable and efficient two-step estimation method.

Facilitate the movement between data frames to xts'. Particularly useful when moving from tidyverse to the widely used xts package, which is the input format of choice to various other packages. It also allows the user to use a spread_by argument for a character column xts conversion.

Evaluation of alternatives based on multiple criteria using TOPSIS method.

Interactive laboratory of Time Series based in Box-Jenkins methodology.

This package provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the theft package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable non-additivity via a power transformation of the response. It implements methods from Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165>, including a 1-degree-of-freedom test for row*column non-additivity', linear in the row and column effects.

This package provides a suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.

Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.

This package provides utility functions to help researchers implement best practices for their coding projects. Includes tools for reading and cleaning data files, initializing R projects with a standard folder structure and optional YAML configuration, creating Quarto documents from reproducible templates with optional sample data and custom styling, detecting the execution context across interactive, Quarto', and script-based workflows, splitting data frames into group-level output files, applying analysis functions to each group with optional parallel execution, and rendering syntactic tree diagrams as standalone PNG images via Typst'.

TidyTuesday is a project by the Data Science Learning Community in which they post a weekly dataset in a public data repository (<https://github.com/rfordatascience/tidytuesday>) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source.

This package provides methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) <doi:10.1093/bioinformatics/btz447>.

This package provides functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in tidybayes <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.

Table 1 is the classical way to describe the patients in a clinical study. The amount of splits in the data in such a table is limited. Table1Heatmap draws a heatmap of all crosstables that can be generated with the data. Users can choose between showing the actual crosstables or direction of effect of associations, and highlight associations by number of patients or p-values. v1.2 - fixed "missing "no visible global function definition for ..".

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood for uni- and multivariate mixture models using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025) <doi:10.48550/arXiv.2504.21812>.

This package provides a global-local approximation framework for large-scale Gaussian process modeling. Please see Vakayil and Joseph (2024) <doi:10.1080/00401706.2023.2296451> for details. This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873.

This package implements tic-tac-toe game to play on console, either with human or AI players. Various levels of AI players are trained through the Q-learning algorithm.

This package provides drop-in replacements for common R functions (mean(), sum(), sd(), min(), etc.) that default to na.rm = TRUE and issue warnings when missing values are removed. It handles some special cases. The table() default is set to useNA = ifany'.

This package provides a general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. tsfgrnn allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation.

This package provides functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

This package provides tools for retrieving Banco de España ('BdE') time series data as tibble objects from bulk CSV files and the Statistics web service (API)'. Bulk CSV functions use stable BdE sequential numbers, while API functions use API series codes. Catalog functions support discovery and local caching. Plotting helpers provide ggplot2 palettes, scales and themes. Banco de España is the national central bank and, within the framework of the Single Supervisory Mechanism ('SSM'), the supervisor of the Spanish banking system alongside the European Central Bank. This package is not sponsored, endorsed or administered by Banco de España'.

This package implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) <doi:10.1007/s10651-025-00670-w>. The method extends Kulldorff (1997) <doi:10.1080/03610929708831995> circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional OpenMP parallelization.

This package provides functions to access historical and real-time national hydrometric data from Water Survey of Canada data sources and then applies tidy data principles.