Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.

Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>. It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see epicalc::lrtest'.

Prevents and detects information leakage in biomedical machine learning. Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection.

This package provides a suite of open-source R functions designed to produce standard metrics for forest management and ecology from forest inventory data. The overarching goal is to minimize potential inconsistencies introduced by the algorithms used to compute and summarize core forest metrics. Learn more about the purpose of the package and the specific algorithms used in the package at <https://github.com/kearutherford/BerkeleyForestsAnalytics>.

This package contains functions mainly focused to plotting bivariate maps.

This package provides tools for extraction and analysis of various n-grams (k-mers) derived from biological sequences (proteins or nucleic acids). Contains QuiPT (quick permutation test) for fast feature-filtering of the n-gram data.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Fast, dependency free, and vectorized base32 encoding and decoding. b32 supports the Crockford, Z, RFC 4648 lower, hex, and lower hex alphabets.

This package provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. (2021) <doi:10.1002/bimj.201900297> and Karrison et al. (2018) <doi:10.1177/1740774518759281>.

Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including bdc and CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.

Run other estimation and simulation software via the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and Monolix'. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be run directly instead of setting up command line usage.

This package implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839> "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with sf for spatial data and ggplot2 for visualization. Supports temporal/streaming data analysis.

This package provides functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) <doi:10.1007/978-0-387-73186-5_11> and Carpenter, Goldstein, and Rasbash (2003) <doi:10.1111/1467-9876.00415>. Currently it supports fitted objects from the lme4 package.

Carry out Bayesian estimation and forecasting for a variety of stochastic mortality models using vague prior distributions. Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265>, the Cairns-Blake-Dowd (2009) <doi:10.1080/10920277.2009.10597538>, the Li-Lee (2005) <doi:10.1353/dem.2005.0021>, and the Plat (2009) <doi:10.1016/j.insmatheco.2009.08.006> models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p à A à T (strata à age à year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported.

The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the Excel based Bifactor Indices Calculator (Dueber, 2017) <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.

Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.

This package provides a GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.

The goal of the package is to provide an easy-to-use method for estimating degrees of relatedness (up to the second degree) for extreme low-coverage data. The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness.

The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.

Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.

Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.

This package provides a lightweight package to access spatial basemaps from open sources such as OpenStreetMap', Carto', Mapbox and others in R.

This package performs BTLLasso as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.

This package performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.