Resurrects the standard plot for shapes established by the base and graphics packages. This is suited to workflows that require plotting using the established and traditional idioms of plotting spatially coincident data where it belongs. This package depends on sf and only replaces the plot method.

Companion package, functions, data sets, examples for the book Patrice Bertail and Anna Dudek (2025), Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted. Kreiss, J.-P. and Paparoditis, E. (2003) <doi:10.1214/aos/1074290332> Politis, D.N., and White, H. (2004) <doi:10.1081/ETC-120028836> Patton, A., Politis, D.N., and White, H. (2009) <doi:10.1080/07474930802459016> Tsybakov, A. B. (2018) <doi:10.1007/b13794> Bickel, P., and Sakov, A. (2008) <doi:10.1214/18-AOS1803> Götze, F. and RaÄ kauskas, A. (2001) <doi:10.1214/lnms/1215090074> Politis, D. N., Romano, J. P., & Wolf, M. (1999, ISBN:978-0-387-98854-2) Carlstein E. (1986) <doi:10.1214/aos/1176350057> Künsch, H. (1989) <doi:10.1214/aos/1176347265> Liu, R. and Singh, K. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Politis, D.N. and Romano, J.P. (1994) <doi:10.1080/01621459.1994.10476870> Politis, D.N. and Romano, J.P. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Patrice Bertail, Anna E. Dudek. (2022) <doi:10.3150/23-BEJ1683> Dudek, A.E., LeÅ kow, J., Paparoditis, E. and Politis, D. (2014a) <https://ideas.repec.org/a/bla/jtsera/v35y2014i2p89-114.html> Beran, R. (1997) <doi:10.1023/A:1003114420352> B. Efron, and Tibshirani, R. (1993, ISBN:9780429246593) Bickel, P. J., Götze, F. and van Zwet, W. R. (1997) <doi:10.1007/978-1-4614-1314-1_17> A. C. Davison, D. Hinkley (1997) <doi:10.2307/1271471> Falk, M., & Reiss, R. D. (1989) <doi:10.1007/BF00354758> Lahiri, S. N. (2003) <doi:10.1007/978-1-4757-3803-2> Shimizu, K. .(2017) <doi:10.1007/978-3-8348-9778-7> Park, J.Y. (2003) <doi:10.1111/1468-0262.00471> Kirch, C. and Politis, D. N. (2011) <doi:10.48550/arXiv.1211.4732> Bertail, P. and Dudek, A.E. (2024) <doi:10.3150/23-BEJ1683> Dudek, A. E. (2015) <doi:10.1007/s00184-014-0505-9> Dudek, A. E. (2018) <doi:10.1080/10485252.2017.1404060> Bertail, P., Clémençon, S. (2006a) <https://ideas.repec.org/p/crs/wpaper/2004-47.html> Bertail, P. and Clémençon, S. (2006, ISBN:978-0-387-36062-1) RaduloviÄ , D. (2006) <doi:10.1007/BF02603005> Bertail, P. Politis, D. N. Rhomari, N. (2000) <doi:10.1080/02331880008802701> Nordman, D.J. Lahiri, S.N.(2004) <doi:10.1214/009053604000000779> Politis, D.N. Romano, J.P. (1993) <doi:10.1006/jmva.1993.1085> Hurvich, C. M. and Zeger, S. L. (1987, ISBN:978-1-4612-0099-4) Bertail, P. and Dudek, A. (2021) <doi:10.1214/20-EJS1787> Bertail, P., Clémençon, S. and Tressou, J. (2015) <doi:10.1111/jtsa.12105> Asmussen, S. (1987) <doi:10.1007/978-3-662-11657-9> Efron, B. (1979) <doi:10.1214/aos/1176344552> Gray, H., Schucany, W. and Watkins, T. (1972) <doi:10.2307/2335521> Quenouille, M.H. (1949) <doi:10.1111/j.2517-6161.1949.tb00023.x> Quenouille, M. H. (1956) <doi:10.2307/2332914> Prakasa Rao, B. L. S. and Kulperger, R. J. (1989) <https://www.jstor.org/stable/25050735> Rajarshi, M.B. (1990) <doi:10.1007/BF00050835> Dudek, A.E. Maiz, S. and Elbadaoui, M. (2014) <doi:10.1016/j.sigpro.2014.04.022> Beran R. (1986) <doi:10.1214/aos/1176349847> Maritz, J. S. and Jarrett, R. G. (1978) <doi:10.2307/2286545> Bertail, P., Politis, D., Romano, J. (1999) <doi:10.2307/2670177> Bertail, P. and Clémençon, S. (2006b) <doi:10.1007/0-387-36062-X_1> RaduloviÄ , D. (2004) <doi:10.1007/BF02603005> Hurd, H.L., Miamee, A.G. (2007) <doi:10.1002/9780470182833> Bühlmann, P. (1997) <doi:10.2307/3318584> Choi, E., Hall, P. (2000) <doi:10.1111/1467-9868.00244> Efron, B., Tibshirani, R. (1993, ISBN:9780429246593) Bertail, P., Clémençon, S. and Tressou, J. (2009) <doi:10.1007/s10687-009-0081-y> Bertail, P., Medina-Garay, A., De Lima-Medina, F. and Jales, I. (2024) <doi:10.1080/02331888.2024.2344670>.

Detection of a statistically significant trend in the data provided by the user. This is based on the a signed test based on the binomial distribution. The package returns a trend test value, T, and also a p-value. A T value close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing trend. A T value close to 0 indicates no trend. There is also a command to visualize the trend. A test data set called gtsa_data is also available, which has global mean temperatures for January, April, July, and October for the years 1851 to 2022. Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).

Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian aggregator that is regularized by analyzing the forecasters disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters probability predictions (p) of a future binary event and b) the forecasters common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example.

View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate.

MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.

MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n à n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-Garcà a (2021) <arXiv:2007.11919> (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-Garcà a (2021) <arXiv:2007.11919> (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes runjags to run Gibbs sampling with parallel chains. Options for different covariances/relationship structures between parameters of interest.

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Download and read US Census Bureau data relationship files. Provides support for cleaning and using block assignment files since 2010, as described in <https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>. Also includes support for working with block equivalency files, used for years outside of decennial census years.

Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.

Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.

Computes uniform bounds on the distance between the cumulative distribution function of a standardized sum of random variables and its first-order Edgeworth expansion, following the article Derumigny, Girard, Guyonvarch (2023) <doi:10.1007/s13171-023-00320-y>.

Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.

This package provides tools that make it easier to validate data using Benford's Law.

Provide a tool to easily build customized data flows to pre-process large volumes of information from different sources. To this end, bdpar allows to (i) easily use and create new functionalities and (ii) develop new data source extractors according to the user needs. Additionally, the package provides by default a predefined data flow to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual sources (SMS, Email, YouTube comments).

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.

Bayesian regularization for feed-forward neural networks.

Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.

Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like glmnet and ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.

Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.