Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988> and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.

Full implementation of the 28 distributions introduced as benchmarks for nonparametric density estimation by Berlinet and Devroye (1994) <https://hal.science/hal-03659919>. Includes densities, cdfs, quantile functions and generators for samples as well as additional information on features of the densities. Also contains the 4 histogram densities used in Rozenholc/Mildenberger/Gather (2010) <doi:10.1016/j.csda.2010.04.021>.

Bit-level reading and writing are necessary when dealing with many file formats e.g. compressed data and binary files. Currently, R connections are manipulated at the byte level. This package wraps existing connections and raw vectors so that it is possible to read bits, bit sequences, unaligned bytes and low-bit representations of integers.

Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

Tool to find where a function has its lowest value(minimum). The functions can be any dimensions. Recommended use is with eps=10^-10, but can be run with 10^-20, although this depends on the function. Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison.

Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).

Randomly reassigns the group identifications to one of the variables of the database, say Treatment, and randomly reassigns the observation numbers of the dataset. Reorders the observations according to these new numbers. Centers each group of Treatment at the grand mean in order to further mask the treatment. An unmasking function is provided so that the user can identify the potential outliers in terms of their original values when blinding is no longer needed. It is suggested that a forward search procedure be performed on the masked data. Details of some forward search functions may be found in <https://CRAN.R-project.org/package=forsearch>.

Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.

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.

Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables.

This is a port of the WTC MATLAB package written by Aslak Grinsted and the wavelet program written by Christopher Torrence and Gibert P. Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses.

This package provides a selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package parallelDist'. A special distance measure for the Gene Ontology is available.

This package provides functions for downloading data from the Bank for International Settlements (BIS; <https://www.bis.org/>) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

Nowcasting right-truncated epidemiological data is critical for timely public health decision-making, as reporting delays can create misleading impressions of declining trends in recent data. This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at <https://wellcomeopenresearch.org/articles/10-614>. The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with epinowcast objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods.

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

This package provides functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Allows the user to generate bootstrap cards within R markdown documents. Intended for use in conjunction with R markdown HTML outputs and other formats that support the bootstrap 4 library.

Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) <doi:10.1101/2022.01.14.476352> for details of the model.

Simulating synthetic clumped isotope dataset, fitting linear regression models under Bayesian and non-Bayesian frameworks, and generating temperature reconstructions for the same two approaches. Please note that models implemented in this package are described in Roman-Palacios et al. (2021) <doi:10.1002/essoar.10507995.1>.

Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.

This package provides a tuneable and interpretable method for relaxing the instrumental variables (IV) assumptions to infer treatment effects in the presence of unobserved confounding. For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. There is no general test of the validity of these IV assumptions for any particular pre-treatment covariate. However, if different pre-treatment covariates give differing causal effect estimates when treated as IVs, then we know at least some of the covariates violate these assumptions. budgetIVr exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption. The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) <doi:10.48550/arXiv.2411.06913>.

This package implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramà rez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramà rez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolà n-Dà az and Rubio-Ramà rez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The bsvarSIGNs package is aligned regarding objects, workflows, and code structure with the R package bsvars by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

This package provides functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.