The sparseMatEst package provides functions for estimating sparse covariance and precision matrices with error control. A false positive rate is fixed corresponding to the probability of falsely including a matrix entry in the support of the estimator. It uses the binary search method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in Kashlak (2019) <arXiv:1903.10988>.

This package provides a Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).

Slack <https://slack.com/> provides a service for teams to collaborate by sharing messages, images, links, files and more. Functions are provided that make it possible to interact with the Slack platform API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like Skype <https://www.skype.com/en/>, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files.

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

Corrects the spelling of a given word in English using a modification of Peter Norvig's spell correct algorithm (see <http://norvig.com/spell-correct.html>) which handles up to three edits. The algorithm tries to find the spelling with maximum probability of intended correction out of all possible candidate corrections from the original word.

Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.

Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).

Package including additional modules for interactive ShinyItemAnalysis application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

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.

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters.

This package provides a collection of forecast verification routines developed for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.

Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

This package provides a system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a â publication readyâ format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.

Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.

Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.

It estimates the parameters of spatio-temporal models with censored or missing data using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and is particularly valuable for models in which the E-step lacks a closed-form expression. It also provides a function to compute the observed information matrix using the method developed by Louis (1982). To assess the performance of the fitted model, case-deletion diagnostics are provided.

An R Shiny application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible. Documentation about SEAHORS is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, <doi:10.5281/zenodo.7674698>).

This package provides a comprehensive logging framework for R applications that provides hierarchical logging levels, database integration, and contextual logging capabilities. The package supports SQLite storage for persistent logs, provides colour-coded console output for better readability, includes parallel processing support, and implements structured error reporting with JSON formatting.

Predicts the presence of signal peptides in eukaryotic protein using hidden semi-Markov models. The implemented algorithm can be accessed from both the command line and GUI.

Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Add indicators (spinner, progress bar, gif) in your shiny applications to show the user that the server is busy. And other tools to let your users know something is happening (send notifications, reports, ...).

Implementation of popular mortality models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. The package supports well-known models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user inputs deaths and exposures and the package outputs the MCMC simulations for each parameter, the log likelihoods and predictions. Moreover, the package includes tools for model selection and Bayesian model averaging by leave future-out validation.

Enhance the bookmarkable state feature of shiny with additional customization such as storage location and storage repositories leveraging the pins package.