Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.

This package provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2025) <doi:10.1080/07350015.2025.2526424>.

Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>. Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) <doi:10.1037/pspp0000508>. Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.

This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.

This package provides a set of tools that enables using OxCal from within R. OxCal (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. OxcAAR allows simple calibration with Oxcal and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.

Automatically adding pkg:: to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the RStudio Markers pane. Lives in the spirit of lintr and styler'. Can also be used for checking which packages are actually used in a project.

Loads and processes huge text corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.

This package provides a flexible framework combining variable screening and random projection techniques for fitting ensembles of predictive generalized linear models to high-dimensional data. Designed for extensibility, the package implements key techniques as S3 classes with user-friendly constructors, enabling easy integration and development of new procedures for high-dimensional applications. For more details see Parzer et al (2024a) <doi:10.48550/arXiv.2312.00130> and Parzer et al (2024b) <doi:10.48550/arXiv.2410.00971>.

This package provides a daemon for checking running and not running processes. It reads the /proc directory every n seconds and does a POSIX regexp on the process names. The daemon runs a user-provided script when it detects a program in the running processes, or an alternate script if it doesn't detect the program. The daemon can only be called by the root user, but can use sudo -u user in the process called if needed.

Oscope is a oscillatory genes identifier in unsynchronized single cell RNA-seq. This statistical pipeline has been developed to identify and recover the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group.

This package provides an optimization method based on sequential quadratic programming for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver, and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large.

This package provides a free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell anabel which method to use, and it takes care of all fitting details. The first two releases of anabel were created and implemented as in (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: rstan', INLA', spBayes', spTimer', spTDyn', CARBayes and CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> and Gemechu, Debusho and Haines (2025) <doi:10.5539/ijsp.v14n1p50> where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 2015 as existing Bland-Altman analysis functions did not calculate confidence intervals. This package was created to rectify this, and create reproducible plots. This package is also available as a module for the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information).

This package provides a simple R wrapper for the Java BiBit algorithm from "A biclustering algorithm for extracting bit-patterns from binary datasets" from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.

Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to igraph <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.

Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and (multivariate) longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population).

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. MOSEK solver is used and needs to be installed; an academic license for MOSEK is free.

Handling the microclimatic data in R. The myClim workflow begins at the reading data primary from microclimatic dataloggers, but can be also reading of meteorological station data from files. Cleaning time step, time zone settings and metadata collecting is the next step of the work flow. With myClim tools one can crop, join, downscale, and convert microclimatic data formats, sort them into localities, request descriptive characteristics and compute microclimatic variables. Handy plotting functions are provided with smart defaults.