Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).

Simple and flexible quizzes in shiny'. Easily create quizzes from various pre-built question and choice types or create your own using htmltools and shiny packages as building blocks. Integrates with larger shiny applications. Ideal for non-web-developers such as educators, data scientists, and anyone who wants to assess responses interactively in a small form factor.

Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. SpatFD is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.

The Sparse Marginal Epistasis Test is a computationally efficient genetics method which detects statistical epistasis in complex traits; see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.

This package provides a set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).

This package contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results.

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

The goal of stim is to provide a function for estimating the Stability Informed Model. The Stability Informed Model integrates stability information (how much a variable correlates with itself in the future) into cross-sectional estimates. Wysocki and Rhemtulla (2022) <https://psyarxiv.com/vg5as>.

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

This package provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows shinySIR to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.

Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.

Because your linear models deserve better than console output. A sleek color palette and kable styling to make your regression results look sharper than they are. Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms, along with a unified interface for model fitting and fabulous LaTeX and console output formatting. See the package website at <https://finitesample.space/snazzier>.

C++ classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See <https://github.com/samuel-watson/SparseChol> for details.

Add shiny inputs with one or more inline buttons that grow and shrink with inputs. Also add tool tips to input buttons and styling and messages for input validation.

Identifying cell types based on expression profiles is a pillar of single cell analysis. scROSHI identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.

The implementation to perform the geometric spatial point analysis developed in Hernández & Solàs (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the sf package. The package has methods to print and plot the results.

Easily use Blueprint', the popular React library from Palantir, in your Shiny app. Blueprint provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in R without writing JavaScript code.

An assortment of helper functions for doing structural equation modeling, mainly by lavaan for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.

Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.

Estimates previously compiled state-space modeling for mouse-tracking experiments using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation.

Acquire hourly meteorological data from stations located all over the world. There is a wealth of data available, with historic weather data accessible from nearly 30,000 stations. The available data is automatically downloaded from a data repository and processed into a tibble for the exact range of years requested. A relative humidity approximation is provided using the August-Roche-Magnus formula, which was adapted from Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.