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This package provides covariate-adjusted comparison of two groups of right censored data, where the binary group variable has separate short-term and long-term effects on the hazard function, while effects of covariates such as age, blood pressure, etc. are proportional on the hazard. The model was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends the two sample version of the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model extends the usual Cox proportional hazards model to allow more flexible hazard ratio patterns, such as gradual onset of effect, diminishing effect, and crossing hazard or survival functions. This package provides the following: 1) point estimates and confidence intervals for model parameters; 2) point estimate and confidence interval of the average hazard ratio; and 3) plots of estimated hazard ratio function with point-wise and simultaneous confidence bands.
Determines the sum of squares of the (2^n)-1 factorial effects in a 2^n factorial experiment using Yates algorithm.
Simple and efficient access to Yahoo Finance's screener API <https://finance.yahoo.com/research-hub/screener/> for querying and retrieval of financial data. The core functionality abstracts the complexities of interacting with Yahoo Finance APIs, such as session management, crumb and cookie handling, query construction, pagination, and JSON payload generation. This abstraction allows users to focus on filtering and retrieving data rather than managing API details. Use cases include screening across a range of security types including equities, mutual funds, ETFs, indices, and futures. The package supports advanced query capabilities, including logical operators, nested filters, and customizable payloads. It automatically handles pagination to ensure efficient retrieval of large datasets by fetching results in batches of up to 250 entries per request. Filters can be dynamically defined to accommodate a wide range of screening needs. The implementation leverages standard HTTP libraries to handle API interactions efficiently and provides support for both R and Python to ensure accessibility for a broad audience.
Deploy, maintain, and invoke models via the Yhat REST API.
For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). The package uses a re-sampling method to obtain stopping boundaries at the interim looks.The output consists of stopping boundaries and observed values of the test statistics at the interim looks, along with nominal p-values defined as the probability of the test exceeding the specific observed test statistic value or critical value, regardless of the test behavior at other looks. The asymptotic validity of the stopping boundaries is established in Yang (2018 <doi:10.1002/sim.7958>).
This package contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://bookdown.org/ndphillips/YaRrr/>.
Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. In case of computational heavy load, the procedures can run in parallel.
Simulation and Inference for SDEs and Other Stochastic Processes.
Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
The Author's personal R Package that contains miscellaneous functions. The current version of package contains miscellaneous functions for brain data to compute Asymmetry Index (AI) and bilateral (L+R) measures and reshape the data.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
Modelling the yield curve with some parametric models. The models implemented are: Nelson, C.R., and A.F. Siegel (1987) <doi: 10.1086/296409>, Diebold, F.X. and Li, C. (2006) <doi: 10.1016/j.jeconom.2005.03.005> and Svensson, L.E. (1994) <doi: 10.3386/w4871>. The package also includes the data of the term structure of interest rate of Federal Reserve Bank and European Central Bank.
Test of linearity originally proposed by Yatchew (1997) <doi:10.1016/S0165-1765(97)00218-8> and improved by de Chaisemartin & D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> to be robust under heteroskedasticity.
Analyze data from behavioral experiments conducted using MED-PC software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.
This package provides a YAML-based mechanism for working with table metadata. Supports compact syntax for creating, modifying, viewing, exporting, importing, displaying, and plotting metadata coded as column attributes. The yamlet dialect is valid YAML with defaults and conventions chosen to improve readability. See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.
Another implementation of general regression neural network in R based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the functional approximation or the classification.
This package provides a fast, correct, safe, and ergonomic YAML 1.2 parser and generator written in Rust'. Convert between YAML and simple R objects with full support for multi-document streams, tags, anchors, and aliases. Offers opt-in handlers for custom tag behavior and round-trips common R data structures. Implements the YAML 1.2.2 specification from the YAML Language Development Team (2021) <https://yaml.org/spec/1.2.2/>. Proudly supported by Posit.
Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system and support for parallel computation.
Obtain historical and near real time data related to stocks, index and currencies from the Yahoo Finance API. This package is community maintained and is not officially supported by Yahoo'. The accuracy of data is only as correct as provided on <https://finance.yahoo.com/>.
Simplify working with the YouTube Analytics API <https://developers.google.com/youtube/analytics>. Collect data for your channel including geography, traffic sources, time period, etc.
Write YAML front matter for R Markdown and related documents. Work with YAML objects more naturally and write the resulting YAML to your clipboard or to YAML files related to your project.
This package provides a number of functions to facilitate extracting information in YAML fragments from one or multiple files, optionally structuring the information in a data.tree'. YAML (recursive acronym for "YAML ain't Markup Language") is a convention for specifying structured data in a format that is both machine- and human-readable. YAML therefore lends itself well for embedding (meta)data in plain text files, such as Markdown files. This principle is implemented in yum with minimal dependencies (i.e. only the yaml packages, and the data.tree package can be used to enable additional functionality).
Analyzing performances of cricketers and cricket teams based on yaml match data from Cricsheet <https://cricsheet.org/>.
Convert YMD format number or string to Date efficiently, using Rust's standard library. It also provides helper functions to handle Date, e.g., quick finding the beginning or end of the given period, adding months to Date, etc.