Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.

This package provides function to estimate multiple change points using marginal likelihood method. See the Manual file in data folder for a detailed description of all functions, and a walk through tutorial. For more information of the method, please see Du, Kao and Kou (2016) <doi:10.1080/01621459.2015.1006365>.

We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.

This package implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), <doi:10.1080/00401706.2020.1739561>. SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor.

Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.

Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called signature (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al. (2022) <DOI:10.1038/s41586-022-04738-6>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

This package provides wrappers for scclust', a C library for computationally efficient size-constrained clustering with near-optimal performance. See <https://github.com/fsavje/scclust> for more information.

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

This package provides a scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via PyTorch and reticulate', which can be run on CPUs or GPUs.

This package provides a single, phenome-wide permutation of large-scale biobank data. When a large number of phenotypes are analyzed in parallel, a single permutation across all phenotypes followed by genetic association analyses of the permuted data enables estimation of false discovery rates (FDRs) across the phenome. These FDR estimates provide a significance criterion for interpreting genetic associations in a biobank context. For the basic permutation of unrelated samples, this package takes a sample-by-variable file with ID, genotypic covariates, phenotypic covariates, and phenotypes as input. For data with related samples, it also takes a file with sample pair-wise identity-by-descent information. The function outputs a permuted sample-by-variable file ready for genome-wide association analysis. See Annis et al. (2021) <doi:10.21203/rs.3.rs-873449/v1> for details.

Easily create alerts, notifications, modals, info tips and loading screens in Shiny'. Includes several options to customize alerts and notifications by including text, icons, images and buttons. When wrapped around a Shiny output, loading screen is automatically displayed while the output is being recalculated.

This package provides a test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.

Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. This tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.

Character vector to numerical translation in Euros from Spanish spelled monetary quantities. Reverse translation from integer to Spanish. Upper limit is up to the millions range. Geocoding via Cadastral web site.

Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641> and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree and a multiple regression model.

Handles both vector and matrices, using a flexible S4 class for automatic differentiation. The method used is forward automatic differentiation. Many functions and methods have been defined, so that in most cases, functions written without automatic differentiation in mind can be used without change.

ML and GM estimation and diagnostic testing of econometric models for spatial panel data.

Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.

Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

This package provides a web-based shiny interface for the StepReg package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats.

This htmlwidget provides pan and zoom interactivity to R graphics, including base', lattice', and ggplot2'. The interactivity is provided through the svg-pan-zoom.js library. Various options to the widget can tailor the pan and zoom experience to nearly any user desire.