Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.

This package implements an anomaly detection algorithm based on mutual reachability minimum spanning trees: deadwood trims protruding tree segments and marks small debris as outliers; see Gagolewski (2026) <https://deadwood.gagolewski.com/>. More precisely, the use of a mutual reachability distance pulls peripheral points farther away from each other. Tree edges with weights beyond the detected elbow point are removed. All the resulting connected components whose sizes are smaller than a given threshold are deemed anomalous. The Python version of deadwood is available via PyPI'.

Fast, flexible and user-friendly tools for distribution comparison through direct density ratio estimation. The estimated density ratio can be used for covariate shift adjustment, outlier-detection, change-point detection, classification and evaluation of synthetic data quality. The package implements multiple non-parametric estimation techniques (unconstrained least-squares importance fitting, ulsif(), Kullback-Leibler importance estimation procedure, kliep(), spectral density ratio estimation, spectral(), kernel mean matching, kmm(), and least-squares hetero-distributional subspace search, lhss()). with automatic tuning of hyperparameters. Helper functions are available for two-sample testing and visualizing the density ratios. For an overview on density ratio estimation, see Sugiyama et al. (2012) <doi:10.1017/CBO9781139035613> for a general overview, and the help files for references on the specific estimation techniques.

This package provides a system designed for detecting concept drift in streaming datasets. It offers a comprehensive suite of statistical methods to detect concept drift, including methods for monitoring changes in data distributions over time. The package supports several tests, such as Drift Detection Method (DDM), Early Drift Detection Method (EDDM), Hoeffding Drift Detection Methods (HDDM_A, HDDM_W), Kolmogorov-Smirnov test-based Windowing (KSWIN), Adaptive WINdowing (ADWIN) and Page Hinkley (PH) tests. The methods implemented in this package are based on established research and have been demonstrated to be effective in real-time data analysis. For more details on the methods, please check to the following sources. KobyliÅ ska et al. (2023) <doi:10.48550/arXiv.2308.11446>, S. Kullback & R.A. Leibler (1951) <doi:10.1214/aoms/1177729694>, Gama et al. (2004) <doi:10.1007/978-3-540-28645-5_29>, Baena-Garcia et al. (2006) <https://www.researchgate.net/publication/245999704_Early_Drift_Detection_Method>, Frà as-Blanco et al. (2014) <https://ieeexplore.ieee.org/document/6871418>, Bifet and Gavalda (2007) <doi:10.1137/1.9781611972771>, Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Montiel et al. (2018) <https://jmlr.org/papers/volume19/18-251/18-251.pdf>.

Mechanistically models/predicts the phenology (macro-phases) of 10 crop plants (trained on a big dataset over 80 years derived from the German weather service (DWD) <https://opendata.dwd.de/>). Can be applied for remote sensing purposes, dynamically check the best subset of available covariates for the given dataset and crop.

Builds interactive d3.js hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...

Designed for genomic and proteomic data analysis, enabling unbiased PubMed searching, protein interaction network visualization, and comprehensive data summarization. This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas et al. (2025) <doi:10.1039/D5MO00160A>. Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) <doi:10.1093/nar/gkac1000> - Winter (2017) <doi:10.32614/RJ-2017-066>.

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086> (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, the heterogeneity test, the marginal-change test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>, and the strength test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns.

Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).

Calculates distances from point locations to features. The usual approach for eg. resource selection function analyses is to generate a complete distance to features surface then sample it with your observed and random points. Since these raster based approaches can be pretty costly with large areas, and often lead to memory issues in R, the distanceto package opts to compute these distances using efficient, vector based approaches. As a helper, there's a decidedly low-res raster based approach for visually inspecting your region's distance surface. But the workhorse is distance_to.

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Generates an RMarkdown data report with two components: a summary of an input dataset and a diff of the dataset relative to an old version.

This package performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>. Drifting Markov models are described in: Vergne, N. (2008) <doi:10.2202/1544-6115.1326>. Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8>. We acknowledge the DATALAB Project <https://lmrs-num.math.cnrs.fr/projet-datalab.html> (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005).

Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data.

Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same.

R package to build and simulate deterministic compartmental models that can be non-Markovian. Length of stay in each compartment can be defined to follow a parametric distribution (d_exponential(), d_gamma(), d_weibull(), d_lognormal()) or a non-parametric distribution (nonparametric()). Other supported types of transition from one compartment to another includes fixed transition (constant()), multinomial (multinomial()), fixed transition probability (transprob()).

This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

This package provides a distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).

This package implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 <doi:10.1016/j.dendro.2007.05.003>. For more details about the package please see: Van der Maaten et al. 2016 <doi:10.1016/j.dendro.2016.06.001>.

Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the dirichletprocess package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Profiles R data frames as compact data fingerprints using schema, shape, missingness, distribution, category, uniqueness, time, and role signals. It compares versions, identifies close relatives in a library of historical data sets, and renders portable HTML cards plus static PNG/PDF lineage figures for reports.

Offers meta programming style tools to generate configurable R functions that produce HTML forms based on table input and SQL meta data. Also generates functions for collecting the parameters of those HTML forms after they are submitted. Useful for quickly generating HTML forms based on existing SQL tables. To use the resultant functions, the output files containing those functions must be read into the R environment (perhaps using base::source()).

The goal of dataspice is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset "README" webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the Schema.org and Ecological Metadata Language standards.