Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

An implementation of functionalities to transform directed graphs that are bound to a set of known forbidden paths. There are several transformations, following the rules provided by Villeneuve and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. The resulting graph is generated in a data-frame format. See rsppfp website for more information, documentation an examples.

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. rumidas also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

This package provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R.

Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. Management occurs on a spatially explicit landscape that is divided into an arbitrary number of farms that can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest genomes are modelled in a way that allows for any number of pest traits with an arbitrary covariance structure that is constructed using an evolutionary algorithm in the mine_gmatrix() function. Simulations are then run using the run_farm_sim() function. This package thereby allows for highly mechanistic social-ecological models of the evolution of pesticide resistance under different types of crop rotation and pesticide application regimes.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

This package provides functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).

Provide function for get data from YouTube Data API <https://developers.google.com/youtube/v3/docs/>, YouTube Analytics API <https://developers.google.com/youtube/analytics/reference/> and YouTube Reporting API <https://developers.google.com/youtube/reporting/v1/reports>.

R implementation of the common parsing tools lex and yacc'.

This package contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.

Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) <doi:10.1007/s11222-024-10419-3>.

This package provides functions used in the R: Einführung durch angewandte Statistik (second edition).

Handle JSON-stat format (<https://json-stat.org>) in R. Not all features are supported, especially the extensive metadata features of JSON-stat'.

Calculate the matrices in Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the foundation for many repeat-sales price indexes.

Yandex Clickhouse (<https://clickhouse.com/>) is a high-performance relational column-store database to enable big data exploration and analytics scaling to petabytes of data. Methods are provided that enable working with Yandex Clickhouse databases via DBI methods and using dplyr'/'dbplyr idioms.

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.

This package performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 <doi:10.1111/2041-210X.14066>) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 <doi:10.1111/2041-210X.14478>).

This package performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014)<doi:10.1200/JCO.2014.55.2208>, Uno et al(2022)<https://CRAN.R-project.org/package=survRM2>, Ueno and Morita(2023)<doi:10.1007/s43441-022-00484-z>.

Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.

Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.

Bridge Regression estimation with linear restrictions defined in Yuzbasi et al. (2019) <arXiv:1910.03660>. Special cases of this approach fit the restricted LASSO, restricted RIDGE and restricted Elastic Net estimators.