An ODBC database interface.

This package provides a tool designed to analyze recurrent events when dealing with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). It extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).

This package contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448> Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L> Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276> Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795> Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.

Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). After normalization data can be visualized as boxplot, heatmap or dotplot.

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>.

Apply sensitivity analysis for offline policy evaluation, as implemented in Jung et al. (2017) <arXiv:1702.04690> based on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.

Higher-order spectra or polyspectra of time series, such as bispectrum and bicoherence, have been investigated in abundant literature and applied to problems of signal detection in a wide range of fields. This package aims to provide a simple API to estimate and analyze them. The current implementation is based on Brillinger and Irizarry (1998) <doi:10.1016/S0165-1684(97)00217-X> for estimating bispectrum or bicoherence, Lii and Helland (1981) <doi:10.1145/355958.355961> for cross-bispectrum, and Kim and Powers (1979) <doi:10.1109/TPS.1979.4317207> for cross-bicoherence.

This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .

This package provides estimation and data generation tools for several new regression models, including the gamma, beta, inverse gamma and beta prime distributions. These models can be parameterized based on the mean, median, mode, geometric mean and harmonic mean, as specified by the user. For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>.

This package provides methods for calculating diversity indices on numerical matrices, based on information theory, following Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini et al. (2021) <doi:10.1101/2021.01.23.427872>.

Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.

Interface to the Dryad "Solr" API, their "OAI-PMH" service, and fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of data underlying scientific publications.

The goal of the rbrsa package is to provide automated access to banking sector data from the Turkish Banking Regulation and Supervision Agency (BRSA, known as BDDK in Turkish). The package retrieves tables from two distinct publication portals maintained by the BRSA: The Monthly Bulletin Portal <https://www.bddk.org.tr/bultenaylik> and The FinTurk Data System <https://www.bddk.org.tr/BultenFinturk>.

Generate SpatRaster objects, as defined by the terra package, from digital images, using a specified spatial object as a geographical reference.

This package provides a wrapper for running the bundled Open-WBO Maximum Satisfiability (MaxSAT) solver (<https://github.com/sat-group/open-wbo>). Users can pass command-line arguments to the solver and capture its output as a character string or file.

High level and easy HTTP client for R'. Provides functions for building HTTP queries, including query parameters, body requests, headers, authentication, and more.

QuantLib bindings are provided for R using Rcpp via an updated variant of the header-only Quantuccia project (put together initially by Peter Caspers) offering an essential subset of QuantLib (and now maintained separately for the calendaring subset). See the included file AUTHORS for a full list of contributors to both QuantLib and Quantuccia'. Note that this package provided an initial viability proof, current work is done (via approximately quarterly releases tracking QuantLib') in the smaller package qlcal which is generally preferred.

This package provides a pair of functions for calculating mean residual life (MRL) , median residual life, and percentile residual life using the outputs of either the flexsurv package or parameters provided by the user. Input information about the distribution, the given life value, the percentile, and the type of residual life, and the function will return your desired values. For the flexsurv option, the function allows the user to input their own data for making predictions. This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.

This package implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.

This package provides a method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in Wrobel et al. (2019) <doi:10.1111/biom.12963> and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology.

Read Acoustic HAC format.

Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.

Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U, L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures. rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta". The Restricted Cubic Splines method were based on Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>, and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.