This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.

This package provides methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term.

This package provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin.

This package provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented.

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the JAGS software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

This package implements collaborative filtering methods for recommendation systems based on user-item interaction data. Supports both explicit feedback (ratings) and implicit feedback (consumption). The package uses efficient sparse matrix representations and provides incremental updates for users, items, and similarity structures through an R6 class-based architecture. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.

Implementations of the family of map() functions with frequent saving of the intermediate results. The contained functions let you start the evaluation of the iterations where you stopped (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter.

Standardised mapping of dates onto a discrete annual grid, together with exact date and duration arithmetic. This matters when the primary unit is years but the input data uses dates. Examples are actuarial mortality experience analysis and valuation of life assurance and annuities, for which mortality rates are defined per year but experience and valuation data are typically defined using dates.

Detects regions of differential abundance in single-cell transcriptomic data by applying a pre-trained neural network model to the labels of each cell's nearest neighbours. Tests for both local and global differential abundance, controlling the false discovery rate with the Benjamini-Yekutieli procedure. The method is described in Hall and Castellano (2023) <doi:10.1101/2023.05.05.539427>.

Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>, to fetch observations across economic series; find information about different economic sources, releases, series, etc.; conduct searches by series name, attributes, or tags; and determine the latest updates. Includes functions for creating panels of related variables with minimal effort and datasets containing data sources, releases, and popular FRED tags.

This package provides high-performance online changepoint detection in univariate and multivariate data streams. Implements efficient C++ backends for the focus', md-focus and np-focus algorithms, with an R interface for real-time monitoring and offline analysis. The package bundles code from Qhull <http://www.qhull.org/>, by C. B. Barber and The Geometry Center. See inst/COPYRIGHTS for details.

Contingency Tables are a pain to work with when you want to run regressions. This package takes them, flattens them into a long data frame, so you can more easily analyse them! As well, you can calculate other related statistics. All of this is done so in a tidy manner, so it should tie in nicely with tidyverse series of packages.

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.

This package provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.

This package provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded, discrete, and zero-inflated data. See Geenens and Wang (2018) <doi:10.48550/arXiv.1602.04862>, Geenens (2014) <doi:10.48550/arXiv.1303.4121>, Nagler (2018a) <doi:10.48550/arXiv.1704.07457>, Nagler (2018b) <doi:10.48550/arXiv.1705.05431>.

Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.

This package provides a fast, lightweight, object-oriented approach to logging based on the widely-emulated log4j system and etymology. Loggers write messages to one or more destinations, such as the console, a file, or a remote server, using a format you control. Messages below the chosen severity threshold are discarded cheaply, so logging calls can be left in production code.

Describes spatial patterns of categorical raster data for any defined regular and irregular areas. Patterns are described quantitatively using built-in signatures based on co-occurrence matrices but also allows for any user-defined functions. It enables spatial analysis such as search, change detection, and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).

Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.

An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.

Exact automatic differentiation for R functions. Provides a composable derivative operator D that computes gradients, Hessians, Jacobians, and arbitrary-order derivative tensors at machine precision. D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for skewness of maximum likelihood estimators, and so on to any order. Works through any R code including loops, branches, and control flow.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

This package provides a growing collection of personal utility functions. Currently provides tools to parse Rich Text Format (RTF) files and extract their tables into data frames, automatically detecting header rows, merging multi-page tables, and resolving merged cells. Particularly useful for tables produced by SAS or by the r2rtf package, which are commonly used for clinical trial and regulatory reporting.