Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates. An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates the correct standardised inputs. This work is not affiliated with the development of NHS Predict and its underlying statistical model. Details on NHS Predict can be found at: <doi:10.1186/bcr2464>. The web version of NHS Predict': <https://breast.predict.nhs.uk/>. A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions, and an additional dataset is provided for running example with the dedicated SCAN function.

This package provides functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).

This package provides deterministic approximation methods for use with the nimble package. These include Laplace approximation and higher-order extension of Laplace approximation using adaptive Gauss-Hermite quadrature (AGHQ), plus nested deterministic approximation methods related to the INLA approach. Additional information is available in the NIMBLE User Manual and a nimbleQuad tutorial, both available at <https://r-nimble.org/documentation.html>.

Stanford CoreNLP annotation client. Stanford CoreNLP <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all NLP tools from the Stanford Natural Language Processing Group, including a part-of-speech (POS) tagger, a named entity recognizer (NER), a parser, and a coreference resolution system, and provides model files for the analysis of English. More information can be found in the README.

This package provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.

Wraps the nametag library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.

Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.

An API client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resources) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web-based data viewer and web access, please see <https://power.larc.nasa.gov/>.

Interface to gather news from the News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.

Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.

Classify occurrence records based on confidence levels of species identification. In addition, implement tools to filter occurrences inside grid cells and to manually check for possibles errors with an interactive shiny application.

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.

Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See http://www.cdc.gov/nchs/nhanes.htm for details.

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes.

This package provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>, the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>, and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.

Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.

In semi-structured interviews that use the framework method, it is not always clear how refinements to interview questions affect the decision of when to stop interviews. The trend of novel and duplicate interview codes (novel codes are information that other interviewees have not previously mentioned) provides insight into the richness of qualitative information. This package provides tools to visualise when refinements occur and how that affects the trends of novel and duplicate codes. These visualisations, when used progressively as new interviews are finished, can help the researcher to decide on a stopping point for their interviews. For context, see Wong et al., (2023) <doi:10.1177/16094069231220773>.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.

The raw dataset and model used in Lai et al. (2021) Decoupled responses of native and exotic tree diversities to distance from old-growth forest and soil phosphorous in novel secondary forests. Applied Vegetation Science, 24, e12548.

Converts number spellings into their equivalent numbers. Supports numbers written in English, French, or Spanish.

This package provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

This package implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.