Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

This package provides an interactive Kanban board widget for shiny applications. It allows users to manage tasks using a drag-and-drop interface and offers customizable styling options. shinykanban is ideal for project management, task tracking, and agile workflows within shiny apps.

This package provides a topological version of k-NN: An abstract model is build as 2-dimensional self-organising map. Samples of unknown class are predicted by mapping them on the SOM and analysing class membership of neurons in the neighbourhood.

In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The samon package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).

This package contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.

Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. singleCellHaystack predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. singleCellHaystack uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of singleCellHaystack we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The spinifex packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the tourr package Wickham et al. <doi:10.18637/jss.v040.i02>. spinifex builds on tourr and can render tours with gganimate and plotly graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) <doi:10.32614/RJ-2020-027>.

The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.

This package provides a Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.

Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.

Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species. (i) Age class determination. (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH). <https://github.com/forestscientist/StemAnalysis>.

This package provides additional convenience functions for gtsummary (Sjoberg et al. (2021) <doi:10.32614/RJ-2021-053>) & gt tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.

This package provides a set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data.

This package implements S-type ridge regression, a robust and multicollinearity-aware linear regression estimator that combines S-type robust weighting (via the Stype.est package) with ridge penalization; automatically selects the ridge parameter using the ridgregextra approach targeting a close to 1 variance inflation factor (VIF), and returns comprehensive outputs (coefficients, fitted values, residuals, mean squared error (MSE), etc.) with an easy x/y interface and optional user-supplied weights. See Sazak and Mutlu (2021) <doi:10.1080/03610918.2021.1928196>, Karadag et al. (2023) <https://CRAN.R-project.org/package=ridgregextra> and Sazak et al. (2025) <https://CRAN.R-project.org/package=Stype.est>.

We provide functions for computing the decision boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen (2010, <doi:10.1002/sim.4036>).

Create carousels using the JavaScript library Swiper and the package htmlwidgets'. The carousels can be displayed in the RStudio viewer pane, in Shiny applications and in R markdown documents. The package also provides a RStudio addin allowing to choose image files and to display them in the viewer pane.

Single-index mixture cure models allow estimating the probability of cure and the latency depending on a vector (or functional) covariate, avoiding the curse of dimensionality. The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (<https://ruc.udc.es/dspace/handle/2183/37035>). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health.

This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.

Network sparsification with a variety of novel and known network sparsification techniques. All network sparsification techniques reduce the number of edges, not the number of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. This package is based on the work of Spielman, D., Srivastava, N. (2009)<arXiv:0803.0929>. Koutis I., Levin, A., Peng, R. (2013)<arXiv:1209.5821>. Toivonen, H., Mahler, S., Zhou, F. (2010)<doi:10.1007>. Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.

This package performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.