Modified functions of the package pcalg and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

This package implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.

This package contains the function mice.impute.midastouch(). Technically this function is to be run from within the mice package (van Buuren et al. 2011), type ??mice. It substitutes the method pmm within mice by midastouch'. The authors have shown that midastouch is superior to default pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS.

Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. The test is based on the max-type statistic and its asymptotic distribution under the null hypothesis of no marginal correlation. The bootstrap procedure is used to approximate the null distribution of the test statistic. The package provides a function for performing the test. For more technical details, refer to Zhang and Laber (2014) <doi:10.1080/01621459.2015.1106403>.

Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.

An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <DOI:10.1214/22-BA1310>.

Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.

An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.

Read a table of fixed width formatted data of different types into a data.frame for each type.

This package performs maximal interaction two-mode clustering, permutation tests, scree plots, and interaction visualizations for bicluster analysis. See Ahmed et al. (2025) <doi:10.17605/OSF.IO/AWGXB>, Ahmed et al. (2023) <doi:10.1007/s00357-023-09434-2>, Ahmed et al. (2021) <doi:10.1007/s11634-021-00441-y>.

Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions. Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the Eigen library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.

Create an interactive table of descriptive statistics in HTML. This table is typically used for exploratory analysis in a clinical study (referred to as Table 1').

An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res', and covariates in gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

Statistical or cognitive modeling usually requires a number of more or less arbitrary choices creating one specific path through a garden of forking paths'. The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, <doi:10.1177/1745691616658637>) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation) as described in Singmann et al. (2024, <doi:10.1037/bul0000434>). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR <https://cran.r-project.org/package=MPTinR>. For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is fit_mpt() which performs the multiverse analysis in one call.

Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.

This package provides tools to perform interrupted-time series through a generalised least squares (GLS) framework on linear outcomes. Allows for multiple interventions and a control with ARMA (autoregressive and moving-average) correction. For more details see Lopez Bernal, Cummins, and Gasparrini (2017) <doi:10.1093/ije/dyw098>.

Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.

Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures.

This package implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

Various utilities for the Multiplicative Multinomial distribution.

The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm(), glm() and associated summaries from the package stats'.

This package provides a comprehensive toolkit for missing person identification combining genetic and non-genetic evidence within a Bayesian framework. Computes likelihood ratios (LRs) for DNA profiles, biological sex, age, hair color, and birthdate evidence. Provides decision analysis tools including optimal LR thresholds, error rate calculations, and ROC curve visualization. Includes interactive Shiny applications for exploring evidence combinations. For methodological details see Marsico et al. (2023) <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. (2021) <doi:10.1016/j.fsigen.2021.102519>.

This package provides a suite of conversion functions to create internally standardized spatial polygons data frames. Utility functions use these data sets to return values such as country, state, time zone, watershed, etc. associated with a set of longitude/latitude pairs. (They also make cool maps.).

Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.