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 (2025) <doi:10.1080/01621459.2025.2594191>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Get a current financial year, start of current month, End of current month, start of financial year and end of it. Allow for offset from the date.

Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquà n et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.

This package provides tools to analyze binary graph objects.

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 a box compatible custom language parser for the languageserver package to provide completion and signature hints in code editors.

This package provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, <doi:10.18637/jss.v069.i07>). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation.

This package provides a collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines.

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'.

Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). Estimated dose-response relationships can be bootstrapped and visualized.

This package implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.

This package provides a novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) <doi:10.48550/arXiv.2408.14625>.

This package provides a Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality.

This package provides a wrapper to allow users to download Bus Open Data Service BODS transport information from the API (<https://www.bus-data.dft.gov.uk/>). This includes timetable and fare metadata (including links for full datasets), timetable data at line level, and real-time location data.

Arrays of structured data types can require large volumes of disk space to store. Blosc is a library that provides a fast and efficient way to compress such data. It is often applied in storage of n-dimensional arrays, such as in the case of the geo-spatial zarr file format. This package can be used to compress and decompress data using Blosc'.

Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. bibliometrix provides various routines for importing bibliographic data from SCOPUS', Clarivate Analytics Web of Science (<https://www.webofknowledge.com/>), Digital Science Dimensions (<https://www.dimensions.ai/>), OpenAlex (<https://openalex.org/>), Cochrane Library (<https://www.cochranelibrary.com/>), Lens (<https://lens.org>), and PubMed (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

Calculate the bark beetle phenology based on raster data or point-related data. There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>, RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>, CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>, BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>, Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>, Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>. The package may be expanded by models for other bark beetle species in the future.

Usually, it is difficult to plot choropleth maps for Bangladesh in R'. The bangladesh package provides ready-to-use shapefiles for different administrative regions of Bangladesh (e.g., Division, District, Upazila, and Union). This package helps users to draw thematic maps of administrative regions of Bangladesh easily as it comes with the sf objects for the boundaries. It also provides functions allowing users to efficiently get specific area maps and center coordinates for regions. Users can also search for a specific area and calculate the centroids of those areas.

This package provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The rust package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution, using the generalized ratio-of-uniforms method. See Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987> for details. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).

Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.

Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 <arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). Extensions to this package are planned. For a journal article describing how to use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.

Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.

This package provides functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package.

Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <doi:10.1214/aoms/1177693507> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>. ROPE bounds are based on discussions in Kruschke (2018) <doi:10.1177/2515245918771304>. Methods for determining the number of posterior samples required are described in Doss et al. (2014) <doi:10.1214/14-EJS957>. Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) <doi:10.18637/jss.v068.i04>. Methods for contingency table analysis is described in Gunel et al. (1974) <doi:10.1093/biomet/61.3.545>. Variational Bayes (VB) methods are described in Salimans and Knowles (2013) <doi:10.1214/13-BA858>. Mediation analysis uses the framework described in Imai et al. (2010) <doi:10.1037/a0020761>. The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) <doi:10.1093/biomet/asz006>. Non-parametric survival methods are described in Qing et al. (2023) <doi:10.1002/pst.2256>. Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) <doi:10.1080/03610926.2017.1388402> and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) <doi:10.1080/03610926.2018.1549247>. Correlation analysis methods are carried out by Barch and Chechile (2023) <doi:10.32614/CRAN.package.DFBA>, and described in Lindley and Phillips (1976) <doi:10.1080/00031305.1976.10479154> and Chechile and Barch (2021) <doi:10.1016/j.jmp.2021.102638>. See also Chechile (2020, ISBN: 9780262044585).