An R DataBase Interface ('DBI') compatible interface to various database platforms ('PostgreSQL', Oracle', Microsoft SQL Server', Amazon Redshift', Microsoft Parallel Database Warehouse', IBM Netezza', Apache Impala', Google BigQuery', Snowflake', Spark', SQLite', and InterSystems IRIS'). Also includes support for fetching data as Andromeda objects. Uses either Java Database Connectivity ('JDBC') or other DBI drivers to connect to databases.

Doubly censored data, as described in Chang and Yang (1987) <doi: 10.1214/aos/1176350608>), are commonly seen in many fields. We use EM algorithm to compute the non-parametric MLE (NPMLE) of the cummulative probability function/survival function and the two censoring distributions. One can also specify a constraint F(T)=C, it will return the constrained NPMLE and the -2 log empirical likelihood ratio for this constraint. This can be used to test the hypothesis about the constraint and, by inverting the test, find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence functions of hat F may also be calculated, but currently, the it may be slow.

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>.

This package implements the de-biased estimator for low-rank matrix completion and provides confidence intervals for entries of interest. See: by Chen et al. (2019) <doi:10.1073/pnas.1910053116>, Mai (2021) <arXiv:2103.11749>.

This package provides a metric called Density-Based Clustering Validation index (DBCV) index to evaluate clustering results, following the <https://github.com/pajaskowiak/clusterConfusion/blob/main/R/dbcv.R> R implementation by Pablo Andretta Jaskowiak. Original DBCV index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), <doi:10.1137/1.9781611973440.96>. A more recent article on the DBCV index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), <doi:10.7717/peerj-cs.3095>.

Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.

Generate Rd markup to list methods for a generic function. Makes it easier to document S3, S4, and S7 generics by automatically finding and linking to method documentation.

This package provides functions for computing the density, distribution, and random generation of the Decision Diffusion model (DDM), a widely used cognitive model for analysing choice and response time data. The package allows model specification, including the ability to fix, constrain, or vary parameters across experimental conditions. While it does not include a built-in optimiser, it supports likelihood evaluation and can be integrated with external tools for parameter estimation. Functions for simulating synthetic datasets are also provided. This package is intended for researchers modelling speeded decision-making in behavioural and cognitive experiments. For more information, see Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Voss and Voss (2007) <doi:10.3758/BF03192967>, and Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models DIFM package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.

This package provides a series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain.

This package provides functions and example datasets to run a decision-analytic model for prevention and treatment strategies across depression severity states (sub-clinical, mild, moderate, severe, and recurrent). The package supports scenario analyses (base and alternative inputs) and summarises outcomes such as coverage, adherence, effect sizes, and healthcare costs.

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, DIVINE makes the process more efficient, transparent, and reproducible.

Main function "decode" is used to decode coded key values to plain text. Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on keyvalue objects used for translation. There are several keyvalue objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets.

The DALY Calculator is a free, open-source Graphical User Interface (GUI) for stochastic disability-adjusted life year (DALY) calculation.

Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.

Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution. The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) <arXiv:2103.09872>).

Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) <doi:10.1007/s10750-020-04417-5>.

Mixed model analysis for quantitative genetics with multi-trait responses and pedigree-based partitioning of individual variation into a range of environmental and genetic variance components for individual and maternal effects. Method documented in dmmOverview.pdf; dmm is an implementation of dispersion mean model described by Searle et al. (1992) "Variance Components", Wiley, NY. Dmm() can do MINQUE', bias-corrected-ML', and REML variance and covariance component estimates.

This package provides tools for exploration of R package dependencies. The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way. It also adds some popularity measures for the packages e.g. in the form of download count through the cranlogs package. Uses the CRAN metadata database <http://crandb.r-pkg.org> and Bioconductor metadata <https://bioconductor.org>. Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). The deepdep_shiny() function runs shiny application that helps to produce a nice deepdep plot.

Access the Google Data Commons API V2 <https://docs.datacommons.org/api/rest/v2/>. Data Commons provides programmatic access to statistical and demographic data from dozens of sources organized in a knowledge graph.

Visualizes variables from descriptive tables produced by descsuppR::buildDescrTbl() using ggstatsplot'. It automatically maps each variable to a suitable ggstatsplot plotting function based on the applied or suggested statistical test. Users can override the automatic mapping via a named list of plot specifications. The package supports grouped and ungrouped tables, and forwards additional arguments to the underlying ggstatsplot functions, providing quick, reproducible, and customizable default visualizations for descriptive summaries.

This package provides a full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.

The goal of dlr is to provide a friendly wrapper around the common pattern of downloading a file if that file does not already exist locally.