Collect your data on digital marketing campaigns from Facebook Organic using the Windsor.ai API <https://windsor.ai/api-fields/>.

An R interface to FLINT <https://flintlib.org/>, a C library for number theory. FLINT extends GNU MPFR <https://www.mpfr.org/> and GNU MP <https://gmplib.org/> with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. FLINT implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>. Finally, FLINT provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions <https://dlmf.nist.gov/>. The R interface defines S4 classes, generic functions, and methods for representation and basic operations as well as plain R functions mirroring and vectorizing entry points in the C library.

Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.

This package provides functions for creating flashcard decks of terms and definitions. This package creates HTML slides using revealjs that can be viewed in the RStudio viewer or a web browser. Users can create flashcards from either existing built-in decks or create their own from CSV files or vectors of function names.

This package implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

Collect your data on digital marketing campaigns from Salesforce using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.

This package provides a set of function for clustering data observation with hybrid method Fuzzy ART and K-Means by Sengupta, Ghosh & Dan (2011) <doi:10.1080/0951192X.2011.602362>.

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities.

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.

Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. Such a procedure implicitly assumes that the sample is exchangeable. This package provides a flexible non-parametric test of this exchangeability assumption, allowing the user to specify the feature dependencies by hand as long as features can be grouped into disjoint independent sets. This package also allows users to test a dual hypothesis, which is, given that the sample is exchangeable, does a proposed grouping of the features into disjoint sets also produce statistically independent sets of features? See Aw, Spence and Song (2023) for the accompanying paper.

Finds CRAN packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate CRAN packages matching their specified requirement. findPackage(<string>) returns a data frame of packages with description containing the input string.

Normalizes the data from a file containing the raw values of the SNP probes of microarray data by using the FISH probes and their corresponding copy number.

Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.

This package provides a mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. stale_package_check() tries to identify any library() calls to unused packages.

This package provides a replacement for dplyr::na_if(). Allows you to specify multiple values to be replaced with NA using a single function.

This package provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.

Used for the design and analysis of a 2x2 factorial trial for a time-to-event endpoint. It performs power calculations and significance testing as well as providing estimates of the relevant hazard ratios and the corresponding 95% confidence intervals. Important reference papers include Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609> Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507> Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020) <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.

The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.