This package provides a fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the a la carte embeddings approach developed by Khodak et al. (2018) <doi:10.48550/arXiv.1805.05388> and evaluate hypotheses about covariate effects on embeddings using the regression framework developed by Rodriguez et al. (2021)<doi:10.1017/S0003055422001228>. New version of the package applies a new estimator to measure the distance between word embeddings as described in Green et al. (2025) <doi:10.1017/pan.2024.22>.

Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.

Implementation of the CNAIM standard in R. Contains a series of algorithms which determine the probability of failure, consequences of failure and monetary risk associated with electricity distribution companies assets such as transformers and cables. Results are visualized in an easy-to-understand risk matrix.

This package provides object-oriented database management tools for working with large datasets across multiple database systems. Features include robust connection management for SQL Server and PostgreSQL databases, advanced table operations with bulk data loading and upsert functionality, comprehensive data validation through customizable field type and content validators, efficient index management, and cross-database compatibility. Designed for high-performance data operations in surveillance systems and large-scale data processing workflows.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

This package provides a very simple syntax for the user to generate custom plot(s) without having to remember complicated ggplot2 syntax. The chartql package uses ggplot2 and manages all the syntax complexities internally. As an example, to generate a bar chart of company sales faceted by product category further faceted by season of the year, we simply write: "CHART bar X category, season Y sales".

This package provides a method for modeling genetic data as a combination of discrete layers, within each of which relatedness may decay continuously with geographic distance. This package contains code for running analyses (which are implemented in the modeling language rstan') and visualizing and interpreting output. See the paper for more details on the model and its utility.

Expands the connector <https://github.com/NovoNordisk-OpenSource/connector> package and provides a convenient interface for accessing and interacting with Databricks <https://www.databricks.com> volumes and tables directly from R.

This package provides a system for creating R Markdown reports with a sequential syntax.

The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <doi:10.1111/stan.12355>, of which functions are provided.

Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>, HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).

This package provides a collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, Vidoli and Fusco and Mazziotta <doi:10.1007/s11205-014-0710-y>, Mazziotta and Pareto (2016) <doi:10.1007/s11205-015-0998-2>, Van Puyenbroeck and Rogge <doi:10.1016/j.ejor.2016.07.038> and other authors.

This package provides a collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more.

An implementation of several functions for feature extraction in categorical time series datasets. Specifically, some features related to marginal distributions and serial dependence patterns can be computed. These features can be used to feed clustering and classification algorithms for categorical time series, among others. The package also includes some interesting datasets containing biological sequences. Practitioners from a broad variety of fields could benefit from the general framework provided by ctsfeatures'.

Estimation of quantile regression models for survival data.

This package provides a spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.

Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x> and, Tsay (1992) <doi:10.2307/2347612>.

An implementation of Fan plots for cytometry data in ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Psychometrically analyze latent individual differences related to tasks, interventions, or maturational/aging effects in the context of experimental or longitudinal cognitive research using methods first described by Thomas et al. (2020) <doi:10.1177/0013164420919898>.

Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.

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'.

This package provides a simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems, to break free from the complex computation of existing similarity formulas and the ability to solve big data.

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from AllMetrics package. â input_dfâ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular modelâ s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as â MINâ and other values are denoted as â NAâ . Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as â MAXâ and other values are denoted as â NAâ . â output_dfâ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of â input_dfâ . Values in â output_dfâ are corresponding â NAâ , â MINâ or â MAXâ . Finally, the column containing minimum number of â NAâ values is denoted as the best column. â min_NA_colâ gives the name of the best column (model). â min_NA_valuesâ are the corresponding metrics values. âBestColumn_metricsâ is the data frame (dimension: 1*8) containing different metrics of the best column (model). â best_column_resultsâ is the final result (a list) containing all of these output elements. In special case, if two columns having equal NA', it will be checked among these two column which one is having least NA in first five rows and will be inferred as the best. More details about AllMetrics can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.