Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.

Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.

Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

This package provides a rudimentary sequencer to define, manipulate and mix sound samples. The underlying motivation is to sonify data, as demonstrated in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

RegLog system provides a set of shiny modules to handle register procedure for your users, alongside with login, edit credentials and password reset functionality. It provides support for popular SQL databases and optionally googlesheet-based database for easy setup. For email sending it provides support for emayili and gmailr backends. Architecture makes customizing usability pretty straightforward. The authentication system created with shiny.reglog is designed to be optional: user don't need to be logged-in to access your application, but when logged-in the user data can be used to read from and write to relational databases.

This package provides several methods to integrate functions over the unit sphere and ball in n-dimensional Euclidean space. Routines for converting to/from multivariate polar/spherical coordinates are also provided.

Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.

Starting from a Regression Model, it provides a stepwise procedure to select the linear predictor.

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.

Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).

Detection of item-wise Differential Item Functioning (DIF) in fitted mirt', multipleGroup or bfactor models using score-based structural change tests. Under the hood the sctest() function from the strucchange package is used.

Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.

Create scaled ggplot representations of playing surfaces. Playing surfaces are drawn pursuant to rule-book specifications. This package should be used as a baseline plot for displaying any type of tracking data.

Browser notifications in Shiny apps, using toastr': <https://github.com/CodeSeven/toastr#readme>.

This package provides useful UI components and input widgets for Shiny applications. The offered components allow to apply non-standard operations and view to your Shiny application, but also help to overcome common performance issues.

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. The spinebil package contains methods to evaluate the performance of projection pursuit index functions using tour methods. A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.

This package provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

This package provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

This package provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008) <doi:10.1371/journal.pmed.0050074>.

Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Fast enrichment analysis for locally correlated statistics via circular permutations. The analysis can be performed at multiple significance thresholds for both primary and auxiliary data sets with efficient correction for multiple testing.

Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.