This package provides a Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels.

Collect your data on digital marketing campaigns from bing Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

For studying recurrent disease and death with competing risks, comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events. Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.

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 provides a set of user-friendly functions designed to fill gaps in existing introductory biostatistics R tools, making it easier for newcomers to perform basic biostatistical analyses without needing advanced programming skills. The methods implemented in this package are based on the works: Connor (1987) <doi:10.2307/2531961> Fleiss, Levin, & Paik (2013, ISBN:978-1-118-62561-3) Levin & Chen (1999) <doi:10.1080/00031305.1999.10474431> McNemar (1947) <doi:10.1007/BF02295996>.

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

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 an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

This package provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility functions to work with the performance functions.

Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).

This package provides a highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>, Solymos et al. (2013) <doi:10.1111/2041-210X.12106>, and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.

Write blog posts and web pages in R Markdown. This package supports the static site generator Hugo (<https://gohugo.io>) best, and it also supports Jekyll (<https://jekyllrb.com>) and Hexo (<https://hexo.io>).

Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2019) <doi:10.1080/03610926.2018.1425450> for details.

BabyTime is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time.

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.

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.

Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and Song et al (2020) <doi:10.48550/arXiv.2009.11409>, relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects.

This package provides methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in <doi:10.48550/arXiv.2411.05371> and <doi:10.1002/sim.9164>.

Fetches, harmonizes, and analyses data from the Spanish National Forest Inventory for reproducible, design-aware forest inventory workflows. Computes tree- and stand-level metrics, applies sampling-based expansion factors, estimates volume, and supports extensible processing for external inventory designs with custom sampling schemes and volume equations.

All the seeds do not germinate at a single point in time due to physiological mechanisms determined by temperature which vary among individual seeds in the population. Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) <doi:10.1093/JXB/38.6.1033> <doi:10.1093/jxb/33.2.288>.

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>.

Static code analysis of box modules. The package enhances code quality by providing linters that check for common issues, enforce best practices, and ensure consistent coding standards.

Estimation and interpretation of Bayesian distributed lag interaction models (BDLIMs). A BDLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a categorical variable under four specific patterns of modification. The main function is bdlim(). There are also summary and plotting files. Details on methodology are described in Wilson et al. (2017) <doi:10.1093/biostatistics/kxx002>.

Easily launch, track, and control functions as background-parallel jobs. Includes robust utilities for job status, error handling, resource monitoring, and result collection. Designed for scalable workflows in interactive and automated settings (local or remote). Integrates with multiple backends; supports flexible automation pipelines and live job tracking. For more information, see <https://anirbanshaw24.github.io/bakerrr/>.