This package implements the EM algorithm with one-step Gradient Descent method to estimate the parameters of the Block-Basu bivariate Pareto distribution with location and scale. We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199> "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete Data via the EM Algorithm".

Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.

This package implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables.

This package provides a toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm implements the estimation for large networks efficiently building on the lighthergm and hergm packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.

Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

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.

Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73â 79. <doi:10.3233/MAS-210517>.

This package provides an interface to Bank of Japan <https://www.boj.or.jp> statistics.

This package provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.

Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed.

This package provides methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndtâ Hallâ Hallâ Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.

This package provides a fast, lightweight, and vectorized base 64 engine to encode and decode character and raw vectors as well as files stored on disk. Common base 64 alphabets are supported out of the box including the standard, URL-safe, bcrypt, crypt, BinHex', and IMAP-modified UTF-7 alphabets. Custom engines can be created to support unique base 64 encoding and decoding needs.

This package provides numerous utilities for acquiring and analyzing baseball data from online sources such as Baseball Reference <https://www.baseball-reference.com/>, FanGraphs <https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.

Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided.

The BioTIME database was first published in 2018 and inspired ideas, questions, project and research article. To make it even more accessible, an R package was created. The BioTIMEr package provides tools designed to interact with the BioTIME database. The functions provided include the BioTIME recommended methods for preparing (gridding and rarefaction) time series data, a selection of standard biodiversity metrics (including species richness, numerical abundance and exponential Shannon) alongside examples on how to display change over time. It also includes a sample subset of both the query and meta data, the full versions of which are freely available on the BioTIME website <https://biotime.st-andrews.ac.uk/home.php>.

This package provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the tsibble package, and is designed to integrate within the tidyverse', and tidyverts (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in ggplot2 to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

This package provides a collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods.

This package implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the bigmatch package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.

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>.

This package provides functions for behavior genetics analysis, including variance component model identification [Hunter et al. (2021) <doi:10.1007/s10519-021-10055-x>], calculation of relatedness coefficients using path-tracing methods [Wright (1922) <doi:10.1086/279872>; McArdle & McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>], inference of relatedness, pedigree conversion, and simulation of multi-generational family data [Lyu et al. (2024) <doi:10.1101/2024.12.19.629449>]. For a full overview, see [Garrison et al. (2024) <doi:10.21105/joss.06203>].

Following Arroyo-Maté-Roque (2006), the function calculates the distance between rows or columns of the dataset using the generalized Minkowski metric as described by Ichino-Yaguchi (1994). The distance measure gives more weight to differences between quartiles than to differences between extremes, making it less sensitive to outliers. Further,the function calculates the silhouette width (Rousseeuw 1987) for different numbers of clusters and selects the number of clusters that maximizes the average silhouette width, unless a specific number of clusters is provided by the user. The approach implemented in this package is based on the following publications: Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7>; Ichino-Yaguchi (1994) <doi:10.1109/21.286391>; Arroyo-Maté-Roque (2006) <doi:10.1007/3-540-34416-0_7>.