Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.

This package provides the function fancycut() which is like cut() except you can mix left open and right open intervals with point values, intervals that are closed on both ends and intervals that are open on both ends.

This package provides a collection of utility functions for working with Year Month Day objects. Includes functions for fast parsing of numeric and character input based on algorithms described in Hinnant, H. (2021) <https://howardhinnant.github.io/date_algorithms.html> as well as a branchless calculation of leap years by Jerichaux (2025) <https://stackoverflow.com/a/79564914>.

Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.

Read and write PNG images with arrays, rasters, native rasters, numeric arrays, integer arrays, raw vectors and indexed values. This PNG encoder exposes configurable internal options enabling the user to select a speed-size tradeoff. For example, disabling compression can speed up writing PNG by a factor of 50. Multiple image formats are supported including raster, native rasters, and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit images are also supported. This implementation uses the libspng C library which is available from <https://github.com/randy408/libspng/>.

This package provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. fido replaces the previous package stray'.

This package provides a convenient and user-friendly interface to interact with the Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>. It enables R developers to integrate Firebase Authentication services seamlessly into their projects, allowing for user authentication, account management, and other authentication-related tasks.

Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.

This package provides functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.

This package provides a collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 <doi:10.1016/j.ajog.2022.08.053>) and detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).

This package performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.

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

Estimation and inference using the Fractionally Cointegrated Vector Autoregressive (VAR) model. It includes functions for model specification, including lag selection and cointegration rank selection, as well as a comprehensive set of options for hypothesis testing, including tests of hypotheses on the cointegrating relations, the adjustment coefficients and the fractional differencing parameters. An article describing the FCVAR model with examples is available on the Webpage <https://sites.google.com/view/mortennielsen/software>.

This package provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package deSolve', or a steady-state solver from package rootSolve'. However, the methods can also be used with other types of functions.

An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed).

FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.

Scans all directories and subdirectories of a path for code snippets, R scripts, R Markdown, PDF or text files containing a specific pattern. Files found can be copied to a new folder.

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

Generate decision tables and simulate operating characteristics for phase I dose-finding designs to enable objective comparison across methods. Supported designs include the traditional 3+3, Bayesian Optimal Interval (BOIN) (Liu and Yuan (2015) <doi:10.1158/1078-0432.CCR-14-1526>), modified Toxicity Probability Interval-2 (mTPI-2) (Guo et al. (2017) <doi:10.1002/sim.7185>), interval 3+3 (i3+3) (Liu et al. (2020) <doi:10.1177/0962280220939123>), and Generalized 3+3 (G3). Provides visualization tools for comparing decision rules and operating characteristics across multiple designs simultaneously.

Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace ImageJ (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.

Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>), 2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>, <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>) and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).

This package provides a model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II.

Interactive data visualization for data practitioners. flourishcharts allows users to visualize their data using Flourish graphs that are grounded in data storytelling principles. Users can create racing bar & line charts, as well as other interactive elements commonly found in D3 graphics, easily in R and Python'. The package relies on an enterprise API provided by Flourish', a data visualization platform <https://developers.flourish.studio/api/introduction/>.

Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a shiny app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.