An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.

Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The robomit package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the psacalc command in Stata'. Additionally, robomit offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>. Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: <doi:10.48550/arXiv.2206.02303>.

Keep track of dates in terms of fractional calendar months per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", The Journal of Performance Measurement, 2008. Model dates as of close of business. Perform date arithmetic in units of "months" and "years". Allow "infinite" dates to model "ultimate" time.

Haml is a layer on top of HTML or XML that is designed to express the structure of documents using indentation rather than closing tags. It was originally envisioned as a plugin for Ruby on Rails, but it can function as a stand-alone templating engine.

Using this package, you can fit a random effects model using either the hierarchical credibility model, a combination of the hierarchical credibility model with a generalized linear model or a Tweedie generalized linear mixed model. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see â Exact samples sizes for clinical trials subject to size and power constraintsâ by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Compute alpha and beta contributional diversity metrics, which is intended for linking taxonomic and functional microbiome data. See GitHub repository for the tutorial: <https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) <doi:10.1093/bioinformatics/btac809>.

Shiny apps can often make use of the same key elements, this package provides modules for common tasks (data upload, wrangling data, figure generation and saving the app state), and also a framework for developing. These modules can react and interact as well as generate code to create reproducible analyses.

This package provides a collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

This package provides a fast and flexible implementation of Callaway and Sant'Anna's (2021)<doi:10.1016/j.jeconom.2020.12.001> staggered Difference-in-Differences (DiD) estimators, fastdid reduces the computation time from hours to seconds, and incorporates extensions such as time-varying covariates and multiple events.

Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.

Application of the filtered monotonic polynomial (FMP) item response model to flexibly fit item response models. The package includes tools that allow the item response model to be build on any monotonic transformation of the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.

Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.

This package provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <https://jmlr.org/papers/v18/16-342.html>.

Helper functions to build SQL statements for dbGetQuery or dbSendQuery under program control. They are intended to increase speed of coding and to reduce coding errors. Arguments are carefully checked, in particular SQL identifiers such as names of tables or columns. More patterns will be added as required.

Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.

Estimation of a lognormal - Generalized Pareto mixture via the Expectation-Maximization algorithm. Computation of bootstrap standard errors is supported and performed via parallel computing. Functions for random number simulation and density evaluation are also available. For more details see Bee and Santi (2025) <doi:10.48550/arXiv.2505.22507>.

This package provides functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002 <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping.

It's a Modern K-Means clustering algorithm which works for data of any number of dimensions, has no limit with the number of clusters expected, offers both methods with and without initial cluster centers, and can start with any initial cluster centers for the method with initial cluster centers.

This package provides functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" <doi:10.2307/2998540>.

Allows the user to generate a friendly user interface for emails sending. The user can choose from the most popular free email services ('Gmail', Outlook', Yahoo') and his default email application. The package is a wrapper for the Mailtoui JavaScript library. See <https://mailtoui.com/#menu> for more information.

Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes. Fitting models to data using MLE (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the LDL (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.

This package provides a doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.