Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

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.

Search and download data from the Swiss Federal Statistical Office (BFS) APIs <https://www.bfs.admin.ch/>.

This package provides an interface to Bank of Japan <https://www.boj.or.jp> statistics.

This package provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.

This package contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. Gaussian processes are represented with a Fourier series based on cosine basis functions. Currently the package includes parametric linear models, partial linear additive models with/without shape restrictions, generalized linear additive models with/without shape restrictions, and density estimation model. To maximize computational efficiency, the actual Markov chain Monte Carlo sampling for each model is done using codes written in FORTRAN 90. This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).

This package provides tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software CRExplorer (see <http://crexplorer.net>). Further extension to more plot variants is planned.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.

This package implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.

This package provides tools to deploy R web server applications that follow the _server.yml standard. This standard allows different R server frameworks ('plumber2', fiery', etc.) to be deployed using a common interface. The package supports deployment to DigitalOcean and includes validation tools to ensure _server.yml files are correctly formatted.

This package provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.

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.

Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.

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.

This package contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Doses (BRAID) model of combined action, along with tools for implementing other combination analysis methods, including Bliss independence, combination index, and additional response surface methods.

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

The Biomarker Optimal Segmentation System R package, bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that bossR offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.

The sample size according to the Bethel's procedure.

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.

Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

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.

This package implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) <doi:10.48550/arXiv.2206.01779>.