This package implements a bootstrap aggregated (bagged) version of the k-nearest neighbors survival probability prediction method (Lowsky et al. 2013). In addition to the bootstrapping of training samples, the features can be subsampled in each baselearner to break the correlation between them. The Rcpp package is used to speed up the computation.

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.

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.

Working with reproducible reports or any other similar projects often require to run the script that builds the output file in a specified way. buildr can help you organize, modify and comfortably run those scripts. The package provides a set of functions that interactively guides you through the process and that are available as RStudio Addin, meaning you can set up the keyboard shortcuts, enabling you to choose and run the desired build script with one keystroke anywhere anytime.

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.

When samples contain missing data, are small, or are suspected of bias, estimation of scale reliability may not be trustworthy. A recommended solution for this common problem has been Bayesian model estimation. Bayesian methods rely on user specified information from historical data or researcher intuition to more accurately estimate the parameters. This package provides a user friendly interface for estimating test reliability. Here, reliability is modeled as a beta distributed random variable with shape parameters alpha=true score variance and beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.

This package provides functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.

This package provides a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

This package provides functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.

The function \codebarcode() produces a histogram-like plot of a distribution that shows granularity in the data.

Evaluate, fit, and analyze Hill dose response models (Goutelle et al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred to as four-parameter log-logistic models. Includes tools to invert Hill models, select models based on the Akaike information criterion (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and construct bootstrapped confidence intervals both on the Hill model parameters and values derived from the Hill model parameters.

This package performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Developed for the following tasks. 1- Simulating and computing the maximum likelihood estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010) <doi:10.1016/j.csda.2009.08.004> and conjugate prior. 3- Computing the Bayesian estimator for the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS distribution based on Jeffrey prior given by Achcar (1993) <doi:10.1016/0167-9473(93)90170-X> 5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring scheme.

This package provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables.

An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, subsampling, and multimetric index calculation. Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral. As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) <https://archive.epa.gov/water/archive/web/html/index-14.html>.

This package provides arithmetic functions for R matrix and big.matrix objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.

This package performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).

Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.

This package provides a robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.

It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) <doi:10.1037/hea0001188>.

Package providing a number of functions for working with Two- and Four-parameter Beta and closely related distributions (i.e., the Gamma- Binomial-, and Beta-Binomial distributions). Includes, among other things: - d/p/q/r functions for Four-Parameter Beta distributions and Generalized "Binomial" (continuous) distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r functions for Two- and Four-Parameter Beta distributions parameterized in terms of their means and variances rather than their shape-parameters. - Moment generating functions for Binomial distributions, Beta-Binomial distributions, and observed value distributions. - Functions for estimating classification accuracy and consistency, making use of the Classical Test-Theory based Livingston and Lewis (L&L) and Hanson and Brennan approaches. A shiny app is available, providing a GUI for the L&L approach when used for binary classifications. For url to the app, see documentation for the LL.CA() function. Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>. Lord (1965) <doi:10.1007/BF02289490>. Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.

Verification of continually updating time series data where we expect new values, but want to ensure previous data remains unchanged. Data previously recorded could change for a number of reasons, such as discovery of an error in model code, a change in methodology or instrument recalibration. Monitoring data sources for these changes is not always possible. Other unnoticed changes could include a jump in time or measurement frequency, due to instrument failure or software updates. Functionality is provided that can be used to check and flag changes to previous data to prevent changes going unnoticed, as well as unexpected jumps in time.

This package provides tools to facilitate the access and processing of data from the Central Bank of Brazil API. The package allows users to retrieve economic and financial data, transforming them into usable tabular formats for further analysis. The data is obtained from the Central Bank of Brazil API: <https://api.bcb.gov.br/dados/serie/bcdata.sgs.series_code/dados?formato=json&dataInicial=start_date&dataFinal=end_date>.