T (extent of the primary tumor), N (absence or presence and extent of regional lymph node metastasis) and M (absence or presence of distant metastasis) are three components to describe the anatomical tumor extent. TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) <https://www.austinpublishinggroup.com/biometrics/fulltext/biometrics-v2-id1014.php>.

An implementation of EDM algorithms based on research software developed at the Sugihara Lab ('UCSD/SIO'). Primary methods include Simplex projection from Sugihara & May (1990) <doi:10.1038/344734a0>, Sequential locally-weighted global linear maps S-map': Sugihara (1994) <doi:10.1098/rsta.1994.0106>, Convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, Multiview embedding from Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.

An interactive data visualization and exploration toolkit that implements Breiman and Cutler's original random forest Java based visualization tools in R, for supervised and unsupervised classification and regression within the algorithm random forest.

We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) <doi:10.48550/arXiv.2105.11939>. The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools.

This package provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern tibble data frames. It uses the underlying pxlib C library to handle the low-level file format details and provides a clean, user-friendly R interface.

The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for credit risk scoring models. This package calculates the Kâ S statistic and plots the true-positive rate and false-positive rate to measure the model strength. This package was written with the credit marketer, who uses risk models in conjunction with his campaigns. The users could read more details from Thrasher (1992) <doi:10.1002/dir.4000060408> and pyks <https://pypi.org/project/pyks/>.

Branching process simulation model of infectious disease transmission with flexible parameterisation of epidemiology and targeted interventions, including isolation, contact tracing and quarantine, to reduce transmission, together with functions to evaluate outbreak control. Introduced in Hellewell et al. (2020) <doi:10.1016/S2214-109X(20)30074-7>.

This package provides methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Circular / ring buffers in R and C. There are a couple of different buffers here with different implementations that represent different trade-offs.

S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental gridded datasets with point observations. For details, see Baez-Villanueva et al. (2020) <doi:10.1016/j.rse.2019.111606>. Bugs / comments / questions / collaboration of any kind are very welcomed.

Verified interval arithmetic for R, in the inf-sup (endpoint) representation of the set-based flavor of the interval standard. Every operation returns an enclosure that provably contains the exact result: outward rounding is obtained from the predecessor and successor formulas of Rump, Zimmermann, Boldo and Melquiond (2009) <doi:10.1007/s10543-009-0218-z>, which are valid under round-to-nearest and therefore need no change to the floating-point rounding mode. That mode is not reachable from R, and changing it would not be a local act: it is per-thread state of the processor, so it would govern every floating-point operation executed afterwards on that thread, in this package or anywhere else. Elementary functions are provided at two levels: a fast level over the included correctly rounded binary64 implementation, comprising fifteen kernels from CORE-MATH <doi:10.1109/ARITH54963.2022.00014> and the hardware square root, widened by the pre-registered slack of two outward steps; and a rigorous level over Rmpfr with a directed-rounding bridge, reached by an escalation ladder of precisions when a verdict would otherwise fall inside the slack. Fast-level enclosures retain measured provenance because correct rounding of the included software is verified numerically rather than established here as a theorem for every kernel. On top of the kernel the package builds natural and centered interval extensions of expressions, a monotonicity test, the Hansen-Sengupta interval Newton operator with extended division and epsilon-inflated candidate verification, and a subdivision (paving) engine whose only failure mode is a named abstention with its budget printed. Conformance with IEEE Std 1788.1-2017 <doi:10.1109/IEEESTD.2018.8277144> is not claimed, and the reason is the standard's own: its subclause 1.5 makes conformance a list of requirements that an implementation shall satisfy, with no partial grade to claim. What this package follows, measured one requirement at a time and stated in the package documentation, is the interval type and the decoration system of clause 5, 22 of the 39 arithmetic operations of Table 4.1, and the seven numeric functions of Table 4.3. What it does not provide is the cancellative operations, the interval comparison relations, the text input and output of subclause 6.8, the interchange representation of subclause 7.3, and the tightest accuracy that subclause 6.5.2 requires of the basic operations, which here are one unit in the last place wider at each end.

Enhances the R Optimization Infrastructure ('ROI') package with the SCS solver for solving convex cone problems.

Generates synthetic tabular data from real datasets using Gaussian copula models, with parametric marginal selection for numerical columns and a cumulative-frequency embedding that brings categorical and boolean columns into the same joint copula. Includes a metadata system with column types and primary keys, declarative constraints enforced via rejection sampling, conditional sampling, and quality, validity and privacy reports modeled on those of the SDMetrics library. Inspired by the Python SDV (Synthetic Data Vault) library by DataCebo'; see Patki, Wedge and Veeramachaneni (2016) "The Synthetic Data Vault" <doi:10.1109/DSAA.2016.49>.

Build regular expressions piece by piece using human readable code. This package contains core functionality, and is primarily intended to be used by package developers.

This package provides a collection of functions to estimate Rogers-Castro migration age schedules using Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.

This package provides a complete interface to LibBi', a library for Bayesian inference (see <https://libbi.org> and Murray, 2015 <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi to conduct inference.

This package provides a series of functions that allow users to access the LinkedIn API to get information about connections, search for people and jobs, share updates with their network, and create group discussions. For more information about using the API please visit <https://developer.linkedin.com/>.

Visualizing crystal structures and selected area electron diffraction (SAED) patterns. It provides functions cry_demo() and dp_demo() to load a file in CIF (Crystallographic Information Framework) formats and display crystal structures and electron diffraction patterns. The function dp_demo() also performs simple simulation of powder X-ray diffraction (PXRD) patterns, and the results can be saved to a file in the working directory. The package has been tested on several platforms, including Linux on Crostini with a Coreâ ¢ m3-8100Y Chromebook, I found that even on this low-powered platform, the performance was acceptable. T. Hanashima (2001) <https://www2.kek.jp/imss/pf/tools/sasaki/sinram/sinram.html> Todd Helmenstine (2019) <https://sciencenotes.org/molecule-atom-colors-cpk-colors/> Wikipedia contributors (2023) <https://en.wikipedia.org/w/index.php?title=Atomic_radius&oldid=1179864711>.

This package provides fast procedures for exploring all pairs of cutpoints of a single covariate with respect to survival and determining optimal cutpoints using a hierarchical method and various ordered logrank tests.

Facilitates querying data from the â Facebook Marketing API', particularly for social science research <https://developers.facebook.com/docs/marketing-apis/>. Data from the Facebook Marketing API has been used for a variety of social science applications, such as for poverty estimation (Marty and Duhaut (2024) <doi:10.1038/s41598-023-49564-6>), disease surveillance (Araujo et al. (2017) <doi:10.48550/arXiv.1705.04045>), and measuring migration (Alexander, Polimis, and Zagheni (2020) <doi:10.1007/s11113-020-09599-3>). The package facilitates querying the number of Facebook daily/monthly active users for multiple location types (e.g., from around a specific coordinate to an administrative region) and for a number of attribute types (e.g., interests, behaviors, education level, etc). The package supports making complex queries within one API call and making multiple API calls across different locations and/or parameters.

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. romic takes advantage of these transformations to create interactive shiny apps for exploratory data analysis such as an interactive heatmap.

Honest and nearly-optimal confidence intervals in fuzzy and sharp regression discontinuity designs and for inference at a point based on local linear regression. The implementation is based on Armstrong and Kolesár (2018) <doi:10.3982/ECTA14434>, and Kolesár and Rothe (2018) <doi:10.1257/aer.20160945>. Supports covariates, clustering, and weighting.

This package provides functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.

Utilities for R package development including NEWS.md management, standalone file creation, and code formatting. Supports popular development workflows and integrates with usethis and RStudio'. Includes helper functions for renaming functions and detecting common coding errors.