Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model.

This is a compendium of C++ routines useful for Bayesian statistics. We steal other people's C++ code, repurpose it, and export it so developers of R packages can use it in their C++ code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code.

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>.

Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results.

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of the SortableJS <https://sortablejs.github.io/Sortable/> JavaScript library as an htmlwidget'. You can use this in Shiny apps and widgets, learnr tutorials as well as R Markdown. In addition, provides a custom learnr question type - question_rank() - that allows ranking questions with drag-and-drop.

This package provides functions for retrieving general and specific data from the Norwegian Parliament, through the Norwegian Parliament API at <https://data.stortinget.no>.

This package provides a framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.

Basic and model-based soil physical analyses.

Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while silp may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, omega = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, silp achieves estimations akin in quality to those derived from LMS. In conclusion, the silp package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021)<doi:10.1007/s10869-020-09717-0> Hsiao et al.(2018)<doi:10.1177/0013164416679877>.

Data related to the Salem Witch Trials Datasets and tutorials documenting the witch accusations and trials centered around Salem, Massachusetts in 1692. Originally assembled by Richard B. Latner of Tulane University for his website <https://www2.tulane.edu/~salem/index.html>. The data sets include information on 152 accused witches, members of the Salem Village Committee, signatories of petitions related to the events, and tax data for Salem Village.

This package provides datasets from Vigen (2015) <https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations> rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation.

This package provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.

The StockDistFit package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.

Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.

This package provides a comprehensive suite of functions designed for constructing and managing ShinyItemAnalysis modules, supplemented with detailed guides, ready-to-use templates, linters, and tests. This package allows developers to seamlessly create and integrate one or more modules into their existing packages or to start a new module project from scratch.

An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, SCORPIUS provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) <doi:10.1101/079509>.

Computes Strongest Neighbor Coherence (SNC), a structural diagnostic that replaces Cronbach's alpha using top-k correlation structure. For methodology, see Wells (2025) <https://github.com/TheotherDrWells/snc>.

Constructs cell-typeâ specific gene regulatory networks from single-cell RNA-sequencing data. The method implements the SCORPION algorithm, which first aggregates individual cells into supercells and then applies PANDA (Passing Attributes between Networks for Data Assimilation) to infer transcription factorâ target regulatory relationships. It also provides statistical methods for differential edge analysis.

This package provides functions to combine, normalize and visualize spectral data, perform principal component analysis (PCA), and assemble customizable image grids suitable for publication-quality scientific figures.

Remove batch effects by projecting query batches into the reference batch space.

This package provides data frames that hold certain columns and attributes persistently for data processing in dplyr'.

Calculate and compare lower confidence bounds for binomial series system reliability. The R shiny application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.