This package performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes.

This package provides a function to plot a regression nomogram of regression objects. Covariate distributions are superimposed on nomogram scales and the plot can be animated to allow on-the-fly changes to distribution representation and to enable outcome calculation.

Converting ascii text into (floating-point) numeric values is a very common problem. The fast_float header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in <doi:10.1002/spe.2984>. fast_float is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple LinkingTo: statement.

Create custom keyboard shortcuts to examine code selected in the Rstudio editor. F3 can for example yield str(selection) and F7 open the source code of CRAN and base package functions on github'.

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 contains three functions that access environmental data from any ERDDAPâ ¢ data web service. The rxtracto() function extracts data along a trajectory for a given "radius" around the point. The rxtracto_3D() function extracts data in a box. The rxtractogon() function extracts data in a polygon. All of those three function use the rerddap package to extract the data, and should work with any ERDDAPâ ¢ server. There are also two functions, plotBBox() and plotTrack() that use the plotdap package to simplify the creation of maps of the data.

Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).

We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.

Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). RCTrep offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. RCTrep provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.

Aims at loading Facebook and Instagram advertising data from Smartly.io into R. Smartly.io is an online advertising service that enables advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information). The package offers an interface to query the Smartly.io API and loads data directly into R for further data processing and data analysis.

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

An Rcpp interface for Eunjeon project <http://eunjeon.blogspot.com/>. The mecab-ko and mecab-ko-dic is based on a C++ library, and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct. This package provides part-of-speech tagging and tokenization function for Korean text.

The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA.

Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods.

Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).

This package provides an interface to the Vamp audio analysis plugin system <https://www.vamp-plugins.org/> developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running Vamp plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion.

This package provides functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.

Recursive lists in the form of R objects, JSON', and XML', for use in teaching and examples. Examples include color palettes, Game of Thrones characters, GitHub users and repositories, music collections, and entities from the Star Wars universe. Data from the gapminder package is also included, as a simple data frame and in nested and split forms.

Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.

An R interface to estimate structured additive regression (STAR) models with BayesX'.

This package provides a comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.

This package performs exploratory projection pursuit via REPPlab (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019) <doi:10.1080/03610918.2019.1626880>) using a Shiny app.