This package performs several conventional cross-validation statistical methods for climate-growth model in the climate reconstruction from tree rings, including Sign Test statistic, Reduction of Error statistic, Product Mean Test, Durbin-Watson statistic etc.

This package provides a range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

This package provides a collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more.

The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.

This package provides functions for assigning Clarke or Parkes (Consensus) error grid zones to blood glucose values, and for plotting both types of error grids in both mg/mL and mmol/L units.

Work with Ecological Metadata Language ('EML') files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.

An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) <doi:10.48550/arXiv.1301.2267>. The ESS algorithm is used for model selection in decomposable graphical models.

This package provides a multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2022), On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics, 16, 2206-2251.

Programmatically access the Quickbase JSON API <https://developer.quickbase.com>. You supply parameters for an API call, qbr delivers an http request to the API endpoint and returns its response. Outputs follow tidyverse philosophy.

Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.

Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.

Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings.

This package provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model.

Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.

This package provides a Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.

This package provides for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user-level customization and extension, while simplifying cross-class interoperability.

This package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) pre-whitening approaches to determining trends in climate data.

This package provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. It offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.

The DBI package provides a database interface (DBI) definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.

This package provides functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.

This package provides functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.

Interface to Local Data Bank ('Bank Danych Lokalnych - bdl') API <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like quick plotting and map generating using data from bank.

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) <doi:10.1007/978-3-030-51924-7_4>.

This package provides methods for evaluating the probability mass function, cumulative distribution function, and generating random samples from discrete tempered stable distributions. For more details see Grabchak (2021) <doi:10.1007/s11009-021-09904-3>.