_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-bnnsurvival 0.1.5
Propagated dependencies: r-rcpp@1.1.0 r-prodlim@2025.04.28 r-pec@2025.06.24
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bnnSurvival
Licenses: GPL 3
Build system: r
Synopsis: Bagged k-Nearest Neighbors Survival Prediction
Description:

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.

r-bsvarsigns 2.0
Propagated dependencies: r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-bsvars@3.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bsvars.org/bsvarSIGNs/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Description:

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.

r-blapsr 0.7.0
Propagated dependencies: r-survival@3.8-3 r-sn@2.1.1 r-rspectra@0.16-2 r-matrix@1.7-4 r-mass@7.3-65 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: <https://github.com/oswaldogressani/blapsr>
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Inference with Laplace Approximations and P-Splines
Description:

Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.

r-bullseye 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-polycor@0.8-1 r-labeling@0.4.3 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-dplyr@1.1.4 r-dendser@1.0.3 r-colorspace@2.1-2 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cbhurley.github.io/bullseye/
Licenses: Expat
Build system: r
Synopsis: Visualising Multiple Pairwise Variable Correlations and Other Scores
Description:

We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.

r-bayesiandisaggregation 0.1.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-readxl@1.4.5 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BayesianDisaggregation
Licenses: Expat
Build system: r
Synopsis: Bayesian Methods for Economic Data Disaggregation
Description:

This package implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for economic disaggregation represents an original methodological contribution.

r-brickster 0.2.12
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-nanoarrow@0.7.0-1 r-jsonlite@2.0.0 r-ini@0.3.1 r-httr2@1.2.1 r-glue@1.8.0 r-fs@1.6.6 r-dplyr@1.1.4 r-dbplyr@2.5.1 r-dbi@1.2.3 r-curl@7.0.0 r-cli@3.6.5 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/databrickslabs/brickster
Licenses: FSDG-compatible
Build system: r
Synopsis: R Toolkit for 'Databricks'
Description:

Collection of utilities that improve using Databricks from R. Primarily functions that wrap specific Databricks APIs (<https://docs.databricks.com/api>), RStudio connection pane support, quality of life functions to make Databricks simpler to use.

r-bidsr 0.1.1
Propagated dependencies: r-uuid@1.2-1 r-s7@0.2.1 r-nanotime@0.3.12 r-jsonlite@2.0.0 r-fs@1.6.6 r-fastmap@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://dipterix.org/bidsr/
Licenses: Expat
Build system: r
Synopsis: Brain Imaging Data Structure ('BIDS') Parser
Description:

Parse and read the files that comply with the brain imaging data structure, or BIDS format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) <doi:10.1038/sdata.2016.44>. Provides query functions to extract and check the BIDS entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) <doi:10.1016/j.neuroimage.2020.117341>; see citation("bidsr") for details and attributions.

r-bhat 0.9-12
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=Bhat
Licenses: GPL 2+
Build system: r
Synopsis: General Likelihood Exploration
Description:

This package provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R.

r-bkmr 0.2.2
Propagated dependencies: r-truncnorm@1.0-9 r-tmvtnorm@1.7 r-tidyr@1.3.1 r-tibble@3.3.0 r-nlme@3.1-168 r-mass@7.3-65 r-magrittr@2.0.4 r-fields@17.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/jenfb/bkmr
Licenses: GPL 2
Build system: r
Synopsis: Bayesian Kernel Machine Regression
Description:

Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al (2015) <doi:10.1093/biostatistics/kxu058>.

r-bsw 0.1.2
Propagated dependencies: r-quadprog@1.5-8 r-matrixstats@1.5.0 r-matrix@1.7-4 r-checkmate@2.3.3 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/UdS-MF-IMBEI/BSW
Licenses: GPL 3+
Build system: r
Synopsis: Fitting a Log-Binomial Model Using the Bekhit–Schöpe–Wagenpfeil (BSW) Algorithm
Description:

This package implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

r-bytescircle 1.1.2
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bytescircle
Licenses: GPL 3
Build system: r
Synopsis: Statistics About Bytes Contained in a File as a Circle Plot
Description:

Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc).

r-bcea 2.4.83
Propagated dependencies: r-voi@1.0.3 r-tidyr@1.3.1 r-scales@1.4.0 r-rdpack@2.6.4 r-purrr@1.2.0 r-plotly@4.11.0 r-matrix@1.7-4 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://gianluca.statistica.it/software/bcea/
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Cost Effectiveness Analysis
Description:

This package produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

r-binordnonnor 1.5.2
Propagated dependencies: r-ordnor@2.2.3 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-genord@2.0.0 r-corpcor@1.6.10 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BinOrdNonNor
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Concurrent Generation of Binary, Ordinal and Continuous Data
Description:

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

r-basemodels 1.1.0
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Ying-Ju/basemodels
Licenses: Expat
Build system: r
Synopsis: Baseline Models for Classification and Regression
Description:

Providing equivalent functions for the dummy classifier and regressor used in Python scikit-learn library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the Python implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named dummy_regressor or dummy_classifier that can be used within the caret package pipeline.

r-bbw 0.3.1
Propagated dependencies: r-withr@3.0.2 r-stringr@1.6.0 r-parallelly@1.45.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-cli@3.6.5 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/rapidsurveys/bbw
Licenses: GPL 3
Build system: r
Synopsis: Blocked Weighted Bootstrap
Description:

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys) is implemented. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.

r-bloq 0.1-2
Propagated dependencies: r-mvtnorm@1.3-3 r-maxlik@1.5-2.1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=BLOQ
Licenses: GPL 2+
Build system: r
Synopsis: Methods to Impute and Analyze Data with BLOQ Observations
Description:

This package provides methods for estimating the area under the concentration versus time curve (AUC) and its standard error in the presence of Below the Limit of Quantification (BLOQ) observations. Two approaches are implemented: direct estimation using censored maximum likelihood, and a two-step approach that first imputes BLOQ values using various methods and then computes the AUC using the imputed data. Technical details are described in Barnett et al. (2020), "Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification," Statistics in Biopharmaceutical Research. <doi:10.1080/19466315.2019.1701546>.

r-baldur 0.0.4
Propagated dependencies: r-viridislite@0.4.2 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rdpack@2.6.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-multidplyr@0.1.4 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/PhilipBerg/baldur
Licenses: Expat
Build system: r
Synopsis: Bayesian Hierarchical Modeling for Label-Free Proteomics
Description:

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).

r-bgvar 2.5.9
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-stochvol@3.2.9 r-readxl@1.4.5 r-rcppprogress@0.4.2 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-knitr@1.50 r-gigrvg@0.8 r-coda@0.19-4.1 r-bayesm@3.1-7 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/mboeck11/BGVAR
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Global Vector Autoregressions
Description:

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.

r-baseq 0.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/ambuvjyn/baseq
Licenses: GPL 3
Build system: r
Synopsis: Basic Sequence Processing Tool for Biological Data
Description:

Primarily created as an easy and understanding way to do basic sequences surrounding the central dogma of molecular biology.

r-bmemlavaan 0.7
Propagated dependencies: r-snowfall@1.84-6.3 r-sem@3.1-16 r-rsem@0.5.1 r-mass@7.3-65 r-lavaan@0.6-20 r-amelia@1.8.3
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://bigdatalab.nd.edu
Licenses: GPL 2
Build system: r
Synopsis: Mediation Analysis with Missing Data and Non-Normal Data
Description:

This package provides methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).

r-butterflyoptions 1.0.1
Propagated dependencies: r-tibble@3.3.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=butterflyOptions
Licenses: GPL 3
Build system: r
Synopsis: Trading Butterfly Options Strategies
Description:

Trading of Butterfly Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (â The Bible of Options Strategies (2nd ed.)â , 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

r-bittermelon 2.2.1
Propagated dependencies: r-unicode@16.0.0-1 r-png@0.1-8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://trevorldavis.com/R/bittermelon/
Licenses: Expat
Build system: r
Synopsis: Bitmap Tools
Description:

This package provides functions for creating, modifying, and displaying bitmaps including printing them in the terminal. There is a special emphasis on monochrome bitmap fonts and their glyphs as well as colored pixel art/sprites. Provides native read/write support for the hex and yaff bitmap font formats and if monobit <https://github.com/robhagemans/monobit> is installed can also read/write several additional bitmap font formats.

r-bws 0.1.0
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://cran.r-project.org/package=bws
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Weighted Sums
Description:

An interface to the Bayesian Weighted Sums model implemented in RStan'. It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) <doi:10.3390/ijerph18041373>. This implementation includes an extension to model binary outcome.

r-bayesestdft 1.0.0
Propagated dependencies: r-numderiv@2016.8-1.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/b.scm (guix-cran packages b)
Home page: https://github.com/Roy-SR-007/bayesestdft
Licenses: Expat
Build system: r
Synopsis: Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework
Description:

This package provides a Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.

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