An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying rplum are derived from the rbacon package by the same authors, and there remains a degree of overlap between the two packages.

This R package connects to SWI-Prolog, <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to prolog (consult, query/submit, once, findall).

This package performs RNA emulation and active learning proposed by Heo and Sung (2025) <doi:10.1080/00401706.2024.2376173> for multi-fidelity computer experiments. The RNA emulator is particularly useful when the simulations with different fidelity level are nonlinearly correlated. The hyperparameters in the model are estimated by maximum likelihood estimation.

Interface to SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package rolog instead. The purpose of this package is to provide the Prolog runtime on systems that do not have a software installation of SWI'-'Prolog'.

This package provides a framework for creating high-performance R packages powered by the Rust programming language using the extendr Rust crate. It offers usethis'-like functions to scaffold and develop Rust powered R packages, including utilities for publishing to CRAN, managing dependencies, configuring development environments, and rendering Rust code in knitr documents. Additionally, it provides functions to compile and evaluate Rust code directly from R for interactive development.

This package provides a task-oriented R interface to the RDKit <https://www.rdkit.org> library through its Python API via reticulate'. The package offers high-level cheminformatics functionality, including molecule parsing, descriptor calculation, and fingerprint generation without replicating the native structure of RDKit'.

This package provides a collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are Cache() and prepInputs()'. Cache() allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from terra and raster packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see ?reproducibleOptions()'.

Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps. Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.

Data for the vignette and examples in RFlocalfdr'. Contains a dataset of 1103547 importance values, and the table of variables used in the random forest splits. The data is Chromosome 22 taken from Auton et al. (2015) <doi:10.1038/nature15393>. It also contains a 51 samples by 22283 genes data set taken from Spira et al. (2004) <doi:10.1165/rcmb.2004-0273OC>.

Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (<https://www.synapse.org/Synapse:syn59520082/wiki/628063>). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at <https://herdiantrisufriyana.github.io/rplec/doc/placental_aging_analysis.html>.

This package provides a unified workflow for relational event modeling by re-exporting core functions from remify', remstats', and remstimate'. Supports tie-oriented and actor-oriented modeling with frequentist and Bayesian estimation. Methods are described in Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x> and Stadtfeld and Block (2017) <doi:10.1177/0081175017709295>.

Download the latest data from the Australian Prudential Regulation Authority <https://www.apra.gov.au/> and import it into R as a tidy data frame.

This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

This package provides functions to access data from the Strava v3 API <https://developers.strava.com/>.

This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

This package provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.

Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

This package implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <doi:10.48550/arXiv.1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <doi:10.2139/ssrn.3085672>.

This package contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in Excel', creating date vectors and changing the console prompt to reflect time.

This package provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.

Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible.

This package provides a thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.

An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package RWeka contains the interface code, the Weka jar is in a separate package RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.

This package provides color schemes for maps and other graphics designed by CARTO as described at <https://carto.com/carto-colors/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.