Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. KISSMig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), <doi:10.1111/ecog.00930>.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

This package provides a Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. MADGRAD is a best-of-both-worlds optimizer with the generalization performance of stochastic gradient descent and at least as fast convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation is provided based on Defazio et al (2020) <arxiv:2101.11075>.

This package provides functions to retrieve public data from ORCID (Open Researcher and Contributor ID) records via the ORCID public API. Fetches employment history, education, works (publications, datasets, preprints), funding, peer review activities, and other public information. Returns data as structured data.table objects for easy analysis and manipulation. Replaces the discontinued rorcid package with a modern, CRAN-compliant implementation.

Bland (2009) <doi:10.1136/bmj.b3985> recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of presize is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa.

Computes the Owen's T function or the bivariate normal integral using one of the following: modified Euler's arctangent series, tetrachoric series, or Vasicek's series. For the methods, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026> (or reprint <arXiv:2312.00011> with better typography) and Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.

This package provides tools for detecting, normalizing, classifying, and extracting scholarly identifier strings. The package provides lightweight, dependency-free helpers for common identifier systems such as DOIs, ORCID iDs, ISBNs, ISSNs, arXiv identifiers, and PubMed identifiers. Functions are designed to be vectorized, predictable, and suitable as low-level building blocks for other R packages and data workflows.

Reference data sets of species sensitivities to compare the results of fitting species sensitivity distributions using software such as ssdtools and Burrlioz'. It consists of 17 primary data sets from four different Australian and Canadian organizations as well as five datasets from anonymous sources. It also includes a data set of the results of fitting various distributions using different software.

Collection of model estimation, and model plotting functions related to the STEPCAM family of community assembly models. STEPCAM is a STEPwise Community Assembly Model that infers the relative contribution of Dispersal Assembly, Habitat Filtering and Limiting Similarity from a dataset consisting of the combination of trait and abundance data. See also <doi:10.1890/14-0454.1> for more information.

This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range. Unlike conventional metrics such as TWAP (Time-Weighted Average Price), which focuses solely on time, or VWAP (Volume-Weighted Average Price), which emphasizes volume, TrueWAP captures fluctuating market behavior by reflecting true price movement within high/low performance boundaries.

Mixed effects modeling with warping for functional data using B- spline. Warping coefficients are considered as random effects, and warping functions are general functions, parameters representing the projection onto B- spline basis of a part of the warping functions. Warped data are modelled by a linear mixed effect functional model, the noise is Gaussian and independent from the warping functions.

This package generates ROC plots. Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.

Network Security Services (NSS) is a set of libraries designed to support cross-platform development of security-enabled client and server applications. Applications built with NSS can support SSL v2 and v3, TLS, PKCS #5, PKCS #7, PKCS #11, PKCS #12, S/MIME, X.509 v3 certificates, and other security standards.

This package tracks the Rapid Release channel, which updates frequently.

BED files store ranged genomic data that can be queried even when the files are compressed. iscream can query data from BED files and return them in muliple formats: parsed records or their summary statistics as data frames or GenomicRanges objects, and matrices as matrix, GenomicRanges, or SummarizedExperiment objects. iscream also provides specialized support for importing methylation data.

The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph.

Power and associated functions useful in prospective planning and monitoring of a clinical trial when a recurrent event endpoint is to be assessed by the robust Andersen-Gill model, see Lin, Wei, Yang, and Ying (2010) <doi:10.1111/1467-9868.00259>. The equations developed in Ingel and Jahn-Eimermacher (2014) <doi:10.1002/bimj.201300090> and their consequences are employed.

Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The ambient package provides an interface to the FastNoise C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions.

This package performs requests to the Arctos API to download data. Provides a set of builder classes for performing complex requests, as well as a set of simple functions for automating many common requests and workflows. More information about Arctos can be found in Cicero et al. (2024) <doi:10.1371/journal.pone.0296478> or on their website <https://arctosdb.org/>.

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>.

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

This package provides a reliable and efficient tool for cleaning univariate time series data. It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions.

This package contains the support functions for the Time Series Analysis book. We present a function to calculate MSE and MAE for inputs of actual and forecast values. We also have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series").

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).