An implementation of the selectboost algorithm (Bertrand et al. 2020, Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.

This package creates 3D animated, interactive visualizations that can be viewed in a web browser.

This package provides a pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the SGDinference package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Include interactive sparkline charts <http://omnipotent.net/jquery.sparkline> in all R contexts with the convenience of htmlwidgets'.

This package provides functions for statistical analysis of point processes.

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>.

Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.

This package provides an abstraction for managing, installing, and switching between sets of installed R packages. This allows users to maintain multiple package libraries simultaneously, e.g. to maintain strict, package-version-specific reproducibility of many analyses, or work within a development/production release paradigm. Introduces a generalized package installation process which supports multiple repository and non-repository sources and tracks package provenance.

This is a modification of HDoutliers package. The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) <arXiv:1908.04000> for detecting anomalies in high-dimensional data that addresses these limitations of HDoutliers algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation.

This package provides access to packages developed for downloading, reading and analyzing microdata from household surveys in Integrated System of Household Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics - IBGE. More information can be obtained from the official website <https://www.ibge.gov.br/>.

These are tools that allow users to do time series diagnostics, primarily tests of unit root, by way of simulation. While there is nothing necessarily wrong with the received wisdom of critical values generated decades ago, simulation provides its own perks. Not only is simulation broadly informative as to what these various test statistics do and what are their plausible values, simulation provides more flexibility for assessing unit root by way of different thresholds or different hypothesized distributions.

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.

Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).

This package implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided.

Given a coro asynchronous generator instance that produces text, write that text into a document selection in RStudio and Positron'. This is particularly helpful for streaming large language model responses into the user's editor.

This package implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

This package provides facilities to implement and run population models of stage-structured species...

Calculates a modified Simplified Surface Energy Balance Index (SSEBI) and the Evaporative Fraction (EF) using geospatial raster data such as albedo and surface-air temperature difference (TSâ TA). The SSEBI is computed from albedo and TSâ TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) <doi:10.1016/S1464-1909(99)00128-8>.

This package provides a subgroup identification method for precision medicine based on quantitative objectives. This method can handle continuous, binary and survival endpoint for both prognostic and predictive case. For the predictive case, the method aims at identifying a subgroup for which treatment is better than control by at least a pre-specified or auto-selected constant. For the prognostic case, the method aims at identifying a subgroup that is at least better than a pre-specified/auto-selected constant. The derived signature is a linear combination of predictors, and the selected subgroup are subjects with the signature > 0. The false discover rate when no true subgroup exists is controlled at a user-specified level.

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.