Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".

This package implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia. It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies. This work was inspired by the blog post at <https://www.datawrapper.de/blog/colorblind-check>.

This package provides a basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.

This package provides a simple way to assess the stability of candidate housekeeping genes is implemented in this package.

Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package ChannelAttribution'.

An interface to the cycle routing/data services provided by CycleStreets', a not-for-profit social enterprise and advocacy organisation. The application programming interfaces (APIs) provided by CycleStreets are documented at (<https://www.cyclestreets.net/api/>). The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist. An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles. These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively.

This package creates a new chars class which looks like a string but is actually a vector of individual characters, making strings iterable. This class enables vector operations on strings such as reverse, sort, head, and set operations.

Call the DeOldify <https://github.com/jantic/DeOldify> image colorization API on DeepAI'<https://deepai.org/machine-learning-model/colorizer> to colorize black and white images.

Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information.

This package provides functions to work with directed (asymmetric) and undirected (symmetric) spatial networks. It makes the creation of connectivity matrices easier, i.e. a binary matrix of dimension n x n, where n is the number of nodes (sampling units) indicating the presence (1) or the absence (0) of an edge (link) between pairs of nodes. Different network objects can be produced by chessboard': node list, neighbor list, edge list, connectivity matrix. It can also produce objects that will be used later in Moran's Eigenvector Maps (Dray et al. (2006) <doi:10.1016/j.ecolmodel.2006.02.015>) and Asymetric Eigenvector Maps (Blanchet et al. (2008) <doi:10.1016/j.ecolmodel.2008.04.001>), methods available in the package adespatial (Dray et al. (2023) <https://CRAN.R-project.org/package=adespatial>). This work is part of the FRB-CESAB working group Bridge <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/bridge/>.

It computes full conformal, split conformal and multi-split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contains plot functions to visualize the output of the full and split conformal functions. To guarantee consistency, the package structure mimics the univariate package conformalInference by Ryan Tibshirani. See Lei, Gâ sell, Rinaldo, Tibshirani, & Wasserman (2018) <doi:10.1080/01621459.2017.1307116> for full and split conformal prediction in regression, and Barber, Candès, Ramdas, & Tibshirani (2023) <doi:10.1214/23-AOS2276> for extensions beyond exchangeability.

Conditional moments test, as proposed by Newey (1985) <doi:10.2307/1911011 > and Tauchen (1985) <doi:10.1016/0304-4076(85)90149-6>, useful to detect specification violations for models estimated by maximum likelihood. Methods for probit and tobit models are provided.

We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.

Generate cofeature (feature by sample) matrices. The package utilizes ggplot2::geom_tile() to generate the matrix allowing for easy additions from the base matrix.

Nonparametric change point estimation for survival data based on p-values of exact binomial tests.

An interactive platform for clustering analysis and teaching based on the shiny web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) <doi:10.1007/s11222-007-9033-z> for spectral clustering.

Supports analysis of trends in climate change, ecological and crop modelling.

This package provides a set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individualsâ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.

The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the lme4 package and robust estimators from the rlmer() function in the robustlmm package, as well as the varComprob() function in the robustvarComp package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

An implementation of the Chrome DevTools Protocol', for controlling a headless Chrome web browser.

This package provides a user friendly function crrcbcv to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.

Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) <doi:10.1007/s11634-018-0324-3>.

Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: ConfMatrix (Confusion matrix) and QCCS (Quality Control Columns Set). The ConfMatrix class of objects offers more than 20 methods based on the confusion matrix. The QCCS class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) <doi:10.3390/app9204240> and Canran Liu et al. (2007) <doi:10.1016/j.rse.2006.10.010> for more details. The package was created with R6'.