Calculates several entropy metrics for spatial data inspired by Boltzmann's entropy formula. It includes metrics introduced by Cushman for landscape mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>); and by Gao et al. for landscape gradients (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.

This package provides a chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in PowerBasic by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future.

This package provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024a) <doi:10.48550/arXiv.1902.09608>, Cattaneo, Crump, Farrell and Feng (2024b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2024c) <doi:10.48550/arXiv.1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.

Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.

This package provides methods for choosing the rank of an SVD (singular value decomposition) approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis (at <arXiv:0909.3052>).

Calculates a range of UK freshwater invertebrate biotic indices including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.

This package performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.

This package provides a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution.

This package provides functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).

Real-time quantitative polymerase chain reaction (qPCR) data sets by Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>. This package provides five data sets, one for each PCR target: (i) rat SLC6A14, (ii) human SLC22A13, (iii) pig EMT, (iv) chicken ETT, and (v) human GAPDH. Each data set comprises a five-point, four-fold dilution series. For each concentration there are three replicates. Each amplification curve is 45 cycles long. Original raw data file: <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/MediaObjects/12859_2007_2080_MOESM5_ESM.xls>.

Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).

Handy frameworks, such as error handling and log generation, for batch scripts. Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.

The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption.

This package provides functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.

We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.

This package provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.

This package provides tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and to analyze data from these designs, using aggregate and individual methods such as: difference scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; analytical estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>; empirical Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>; Elo, Hollis (2018) <doi:10.3758/s13428-017-0898-2>; and network-based measures.

Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using rstan (See Stan Development Team (2020) <https://mc-stan.org/>). Functions are provided for estimation, result extraction, model fit statistics, and plottings.

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.

Facilitates retrieval, transformation and analysis of the data from the Barcode of Life Data Systems (BOLD) database <https://boldsystems.org/>. This package allows both public and private user data to be easily downloaded into the R environment using a variety of inputs such as: IDs (processid, sampleid), BINs, dataset codes, project codes, taxonomy, geography etc. It provides frictionless data conversion into formats compatible with other R-packages and third-party tools, as well as functions for sequence alignment & clustering, biodiversity analysis and spatial mapping.

This package provides a set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.