Tidies up the forecasting modeling and prediction work flow, extends the broom package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for various forecasting models, and enables converting forecast objects to "tidy" data frames with sw_sweep'.

This package provides data about the Star Wars movie franchise in a set of relational tables or as a complete DuckDB database. All data was collected from the open source Star Wars API.

This package provides a network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.

In some situations where researchers would like to demonstrate causal effects, it is hard to obtain a sample size that would allow for a well-powered randomized controlled trial. Single case designs are experimental designs that can be used to demonstrate causal effects with only one participant or with only a few participants. The scdtb package provides a suite of tools for analyzing data from studies that use single case designs. The nap() function can be used to compute the nonoverlap of all pairs as outlined by the What Works Clearinghouse (2022) <https://ies.ed.gov/ncee/wwc/Handbooks>. The package also offers the mixed_model_analysis() and cross_lagged() functions which implement mixed effects models and cross lagged analyses as described in Maric & van der Werff (2020) <doi:10.4324/9780429273872-9>. The randomization_test() function implements randomization tests based on methods presented in Onghena (2020) <doi:10.4324/9780429273872-8>. The scdtb() shiny application can be used to upload single case design data and access various scdtb tools for plotting and analysis.

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.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))".

Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html>. You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.

Implementation of the BLEU-Score in C++ to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an RStudio gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.

Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.

Geostatistical modeling and kriging with gridded data using spatially separable covariance functions (Kronecker covariances). Kronecker products in these models provide shortcuts for solving large matrix problems in likelihood and conditional mean, making snapKrig computationally efficient with large grids. The package supplies its own S3 grid object class, and a host of methods including plot, print, Ops, square bracket replace/assign, and more. Our computational methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.

This package provides access to granular sub-national income data from the MCC-PIK Database Of Sub-national Economic Output (DOSE). The package downloads and processes the data from its open repository on Zenodo (<https://zenodo.org/records/13773040>). Functions are provided to fetch data at multiple geographic levels, match coordinates to administrative regions, and access associated geometries.

This package provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The rjags package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Generate syntax for use with the sparklines package for LaTeX.

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.

This package provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.

This package provides a fast and accurate pipeline for single-cell analyses. The scDHA software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).

Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models. Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.

This package provides a modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.

Offers a systematic way for conditional reporting of figures and tables for many (and bivariate combinations of) variables, typically from survey data. Contains interactive ggiraph'-based (<https://CRAN.R-project.org/package=ggiraph>) plotting functions and data frame-based summary tables (bivariate significance tests, frequencies/proportions, unique open ended responses, etc) with many arguments for customization, and extensions possible. Uses a global options() system for neatly reducing redundant code. Also contains tools for immediate saving of objects and returning a hashed link to the object, useful for creating download links to high resolution images upon rendering in Quarto'. Suitable for highly customized reports, primarily intended for survey research.

This package implements a simple, novel clustering algorithm based on optimizing the silhouette width. See <doi:10.1101/2023.11.07.566055> for details.

An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" <arXiv:2006.00267v2> for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().