Automatic generation of finite state machine models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.

This package contains the support functions for the Time Series Analysis book. We present a function to calculate MSE and MAE for inputs of actual and forecast values. We also have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series").

Simplifies and automates the process of exploring and merging data from relational databases. This package allows users to discover table relationships, create a map of all possible joins, and generate executable plans to merge data based on a structured metadata framework.

This package provides functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm by Tikka, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, transportability (Bareinboim and Pearl, 2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, missing data (Mohan, Pearl, and Tian, 2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see (Corander et al., 2019) <doi:10.1016/j.apal.2019.04.004>.

This package provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.

This package provides a collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.

Templates and data files to support "Discrete Choice Analysis with R", Páez, A. and Boisjoly, G. (2023) <doi:10.1007/978-3-031-20719-8>.

This package provides a comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for Cutadapt and DADA2 to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into phyloseq and taxmap objects. For more information on DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) <doi:10.1038/nmeth.3869>. For more details on the demulticoder R package see Sudermann et al. (2025) <doi:10.1094/PHYTO-02-25-0043-FI>.

This package provides a set of functions to perform distribution-free Bayesian analyses. Included are Bayesian analogues to the frequentist Mann-Whitney U test, the Wilcoxon Signed-Ranks test, Kendall's Tau Rank Correlation Coefficient, Goodman and Kruskal's Gamma, McNemar's Test, the binomial test, the sign test, the median test, as well as distribution-free methods for testing contrasts among condition and for computing Bayes factors for hypotheses. The package also includes procedures to estimate the power of distribution-free Bayesian tests based on data simulations using various probability models for the data. The set of functions provide data analysts with a set of Bayesian procedures that avoids requiring parametric assumptions about measurement error and is robust to problem of extreme outlier scores.

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the bnlearn package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the visNetwork package. Further detailed information and examples can be found in our Journal of Statistical Software paper Quesada D., Larrañaga P. and Bielza C. (2025) <doi:10.18637/jss.v115.i06>.

Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.

This package provides a collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.

Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.

In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.

This package provides methods to detect differential item functioning (DIF) in dichotomous, polytomous, and continuous items, using both classical and modern approaches. These include Mantel-Haenszel procedures, logistic regression (including ordinal models), and regularization-based methods such as LASSO. Uniform and non-uniform DIF effects can be detected, and some methods support multiple focal groups. The package also provides tools for anchor purification, rest score matching, effect size estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck (2010, Behavior Research Methods, 42, 847â 862, <doi:10.3758/BRM.42.3.847>) for a general overview.

Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting. For more details see Zyla, J., Szumala, K., Polanski, A., Polanska, J., & Marczyk, M. (2026) <doi:10.1016/j.jocs.2026.102811>.

This package implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.

Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2025) <doi:10.18637/jss.v115.i05>.

This package creates a Dumbbell Plot.

Duplicated data can exist in different rows and columns and user may need to treat observations (rows) connected by duplicated data as one observation, e.g. companies can belong to one family (and thus: be one company) by sharing some telephone numbers. This package allows to find connected rows based on data on chosen columns and collapse it into one row.

Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.