Fast clustering of large datasets by hierarchically merging components of a K-means solution based on the pairwise overlap between the Gaussian mixture components implied by the K-means partition, as proposed by Melnykov and Michael (2020) <doi:10.1007/s00357-019-09314-8>. Implements the DEMP-K merging algorithm with single, Ward's, average, and complete linkages, the overlap map display for selecting the number of clusters, four K-means variants corresponding to Gaussian mixtures with spherical or elliptical, homoscedastic or heteroscedastic components, and a tool for selecting the number of K-means components.

This package performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using EBP estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weight. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs.

seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues.

This package provides ready-to-use datasets from the Korean National Assembly (assemblies 20 through 22, 2016-2026) for teaching quantitative methods in political science. Includes legislator metadata, bill proposals, roll call votes, asset declarations, and policy seminar records. Designed as a Korean politics counterpart to packages like palmerpenguins', enabling students to practice regression, panel data analysis, text analysis, and network analysis with real legislative data. Roll call vote data and spatial voting models are described in Poole and Rosenthal (1985) <doi:10.2307/2111172>. Legislative data is sourced from the Korean National Assembly Open API.

This package provides recent kernel density estimation methods for circular data, including adaptive and higher-order techniques. The implementation is based on recent advances in bandwidth selection and circular smoothing. Key methods include adaptive bandwidth selection methods by ZámeÄ nà k et al. (2024) <doi:10.1007/s00180-023-01401-0>, complete cross-validation by Hasilová et al. (2024) <doi:10.59170/stattrans-2024-024>, Fourier-based plug-in rules by Tenreiro (2022) <doi:10.1080/10485252.2022.2057974>, and higher-order kernels by Tsuruta & Sagae (2017) <doi:10.1016/j.spl.2017.08.003>.

Computing comorbidity indices and scores such as the weighted Charlson score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>) using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>). Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both the Charlson and Elixhauser comorbidity scores.

Implementation of the Dirichlet Random Forest algorithm for compositional response data. Trees are grown using a Dirichlet log-likelihood splitting criterion, with maximum likelihood ('MLE') and method-of-moments ('MOM') parameter estimation. Provides averaging-based predictions (average of responses within terminal nodes), parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes), and distributional predictions represented as a weighted distribution over the training responses. Out-of-bag estimation and impurity- and permutation-based variable importance are also supported. For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910).

In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.

Create interactive flow maps using FlowmapBlue TypeScript library <https://github.com/FlowmapBlue/FlowmapBlue>, which is a free tool for representing aggregated numbers of movements between geographic locations as flow maps. It is used to visualize urban mobility, commuting behavior, bus, subway and air travels, bicycle sharing, human and bird migration, refugee flows, freight transportation, trade, supply chains, scientific collaboration, epidemiological and historical data and many other topics. The package allows to either create standalone flow maps in form of htmlwidgets and save them in HTML files, or integrate flow maps into Shiny applications.

It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

This package provides a wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <DOI:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>.

This package provides infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. It is intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package psychomix and trees based on psychometric models in package psychotree.

Trigger-rally is a 3D rally simulation with great physics for drifting on over 200 maps. Different terrain materials like dirt, asphalt, sand, ice, etc. and various weather, light, and fog conditions give this rally simulation the edge over many other games. You need to make it through the maps in often tight time limits and can further improve by beating the recorded high scores. All attached single races must be finished in time in order to win an event, unlocking additional events and cars. Most maps are equipped with spoken co-driver notes and co-driver icons.

rabbitmqadmin is a tool to manage RabbitMQ broker via management plugin.

It supports many of the operations available in the management UI:

• Listing objects like virtual hosts, users, queues, streams, permissions, policies, and so on.

• Creating objects.

• Deleting objects.

• Access to cluster and node metrics.

• Run health checks.

• Listing feature flag state.

• Listing deprecated features in use across the cluster.

• Definition export, transformations, and import.

• Operations on shovels.

• Operations on federation upstreams and links.

• Closing connections.

• Rebalancing of queue leaders across cluster nodes.

Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc).

This package implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.

This package provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction.

Computes simultaneous prediction and confidence bands for densely sampled functional data on a common grid. The calibration builds on the functional bootstrap approach of Lenhoff et al. (1999) <doi:10.1016/S0966-6362(98)00043-5>; hierarchical measurement designs are motivated by Koska et al. (2023) <doi:10.1016/j.jbiomech.2023.111506>. Independent curves are resampled individually. Clustered data use an intact-subject bootstrap with equal subject weighting, and the clustered prediction target is one future curve from a new subject. Curves are represented by finite Fourier series, and an Rcpp backend performs the bootstrap calibration.

This package implements the MST-kNN clustering algorithm proposed by Inostroza-Ponta (2008) <https://trove.nla.gov.au/work/28729389>. The algorithm determines the number of clusters automatically by recursively intersecting the Minimum Spanning Tree (MST) and the k-Nearest Neighbor (kNN) proximity graphs constructed from a pairwise distance matrix. The value of k is selected via a connectivity criterion (the smallest k such that the kNN graph is connected, bounded by floor(log(n))). The package requires only a distance matrix as input and returns cluster assignments, an igraph network, and partition metadata.

Automates common psychometric workflows for applied researchers, including item descriptives, inter-item correlations, exploratory and confirmatory factor analysis, reliability, multi-group measurement invariance, and alignment optimization. Decision heuristics are informed by procedures such as parallel analysis (Horn, 1965, <doi:10.1007/BF02289447>), multivariate normality diagnostics (Mardia, 1970, <doi:10.1093/biomet/57.3.519>), measurement-invariance fit-change rules (Chen, 2007, <doi:10.1080/10705510701301834>), and alignment optimization (Asparouhov and Muthen, 2014, <doi:10.1080/10705511.2014.919210>), among others. Results can be returned as structured R objects and exported as bilingual reports for transparent analytical documentation.

Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> and Taniguchi et al. (2025) <doi: 10.3389/frai.2024.1477637> for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed.

The ProteinGymR package provides analysis-ready data resources from ProteinGym, generated by Notin et al., 2023, as well as built-in functionality to visualize the data. ProteinGym comprises a collection of benchmarks for evaluating the performance of models predicting the effect of point mutations. This package provides access to 1. deep mutational scanning (DMS) scores from 217 assays measuring the impact of all possible amino acid substitutions across 186 proteins, 2. model performance metrics and prediction scores from 79 variant prediction models in the zero-shot setting and 12 models in the semi-supervised setting.

This package provides functions to access real-time infectious disease data from the disease.sh API', including COVID-19 global, US states, continent, and country statistics, vaccination coverage, influenza-like illness data from the Centers for Disease Control and Prevention (CDC), and more. Also includes curated datasets on a variety of infectious diseases such as influenza, measles, dengue, Ebola, tuberculosis, meningitis, AIDS, and others. The package supports epidemiological research and data analysis by combining API access with high-quality historical and survey datasets on infectious diseases. For more details on the disease.sh API', see <https://disease.sh/>.

Allows the construction selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around to assess its impact on genetic and phenotypic progress. The methodology thereby allows to analyze genetic gain of traits in the breeding goal which are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. The framework is described in Simianer, H., Heise, J., Rensing, S., Pook, T. Geibel, J. and Reimer, C. (2023) <doi:10.1186/s12711-023-00807-0>.