Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of Petri Net objects and visualization of process maps. See also packages edeaR','processmapR', eventdataR and processmonitR'.

This package implements the efficient estimator of bid-ask spreads from open, high, low, and close prices described in Ardia, Guidotti, & Kroencke (JFE, 2024) <doi:10.1016/j.jfineco.2024.103916>. It also provides an implementation of the estimators described in Roll (JF, 1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, Corwin & Schultz (JF, 2012) <doi:10.1111/j.1540-6261.2012.01729.x>, and Abdi & Ranaldo (RFS, 2017) <doi:10.1093/rfs/hhx084>.

BabyTime is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time.

Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.

This package provides a two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

Easily launch, track, and control functions as background-parallel jobs. Includes robust utilities for job status, error handling, resource monitoring, and result collection. Designed for scalable workflows in interactive and automated settings (local or remote). Integrates with multiple backends; supports flexible automation pipelines and live job tracking. For more information, see <https://anirbanshaw24.github.io/bakerrr/>.

Imports benthic count data, reformats this data, and computes environmental inferences from this data.

Model selection by bootstrapping the stepAIC() procedure.

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

It brings together several aspects of biodiversity data-cleaning in one place. bdc is organized in thematic modules related to different biodiversity dimensions, including 1) Merge datasets: standardization and integration of different datasets; 2) pre-filter: flagging and removal of invalid or non-interpretable information, followed by data amendments; 3) taxonomy: cleaning, parsing, and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; 4) space: flagging of erroneous, suspect, and low-precision geographic coordinates; and 5) time: flagging and, whenever possible, correction of inconsistent collection date. In addition, it contains features to visualize, document, and report data quality â which is essential for making data quality assessment transparent and reproducible. The reference for the methodology is Ribeiro and colleagues (2022) <doi:10.1111/2041-210X.13868>.

This package provides tools to generate unique identifier codes and printable barcoded labels for the management of biological samples. The creation of unique ID codes and printable PDF files can be initiated by standard commands, user prompts, or through a GUI addin for R Studio. Biologically informative codes can be included for hierarchically structured sampling designs.

This package provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.

Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".

Real-time quantitative polymerase chain reaction (qPCR) data sets by Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>. This package provides five data sets, one for each PCR target: (i) rat SLC6A14, (ii) human SLC22A13, (iii) pig EMT, (iv) chicken ETT, and (v) human GAPDH. Each data set comprises a five-point, four-fold dilution series. For each concentration there are three replicates. Each amplification curve is 45 cycles long. Original raw data file: <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/MediaObjects/12859_2007_2080_MOESM5_ESM.xls>.

Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) <doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012) <doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) <doi:10.1002/env.2194>.

Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.

Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.

Generates Monte Carlo confidence intervals for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm(). betaMC combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2024 <doi:10.3758/s13428-023-02114-4>) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>) to generate confidence intervals effect sizes in regression.

This package provides tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). bvhar can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Asymptotic simultaneous confidence intervals for comparison of many treatments with one control, for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals. Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.

Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using rstan (See Stan Development Team (2020) <https://mc-stan.org/>). Functions are provided for estimation, result extraction, model fit statistics, and plottings.

BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.