An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.

Generates data from R or JAGS code for use in simulation studies. The data are returned as an nlist::nlists object and/or saved to file as individual .rds files. Parallelization is implemented using the future package. Progress is reported using the progressr package.

Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2020, <doi:10.1177/0959683620913924>).

Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The spatPomp package extends pomp to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.

Estimate average treatment effects (ATEs) in stratified randomized experiments. `sreg` supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units â possibly of unequal size across strata. sreg is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. sreg computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) <doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) <doi:10.48550/arXiv.2204.08356>; Jiang, Linton, Tang, Zhang (2023+) <doi:10.48550/arXiv.2201.13004>; Bai, Jiang, Romano, Shaikh, and Zhang (2024) <doi:10.1016/j.jeconom.2024.105740>; Bai (2022) <doi:10.1257/aer.20201856>; Bai, Romano, and Shaikh (2022) <doi:10.1080/01621459.2021.1883437>; Liu (2024+) <doi:10.48550/arXiv.2301.09016>; and Cytrynbaum (2024) <doi:10.3982/QE2475>.

The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated spatstat package (>= 3.0-2).

Chat with large language models on your machine without internet with complete privacy via ollama', powered by R shiny interface. For more information on ollama', visit <https://ollama.com>.

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

This package provides a simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. SeuratExplorer requires a processed Seurat object, which is saved as rds or qs2 file.

This package provides a combined slider and numeric input for usage in a Shiny app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

Clinical Data Interchange Standards Consortium (CDISC) Standard Data Tabulation Model (SDTM) controlled terminology, 2025-03-25. Source: <https://evs.nci.nih.gov/ftp1/CDISC/SDTM/>.

Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, <https://www.aclweb.org/anthology/D07-1090/>).

Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.

This package provides the hyphenation algorithm used for TeX'/'LaTeX and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the koRpus package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

This package provides functions and methods for estimating phenological dates (green up, start of a season, maturity, senescence, end of a season and dormancy) from (nearly) periodic Earth Observation time series. These dates are critical points of some derivatives of an idealized curve which, in turn, is obtained through a functional principal component analysis-based regression model. Some of the methods implemented here are based on T. Krivobokova, P. Serra and F. Rosales (2022) <https://www.sciencedirect.com/science/article/pii/S0167947322000998>. Methods for handling and plotting Earth observation time series are also provided.

This package provides a set of consistent, opinionated functions to quickly check function arguments, coerce them to the desired configuration, or deliver informative error messages when that is not possible.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

This package implements the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995> to obtain likelihood evaluations and maximum likelihood parameter estimates. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with an arrival distribution for the return and volatility jumps. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. Also includes functions to compute filtering and prediction distribution estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model.

This package performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

The scrapeR package utilizes functions that fetch and extract text content from specified web pages. It handles HTTP errors and parses HTML efficiently. The package can handle hundreds of websites at a time using the scrapeR_in_batches() command.

Strength training prescription using percent-based approach requires numerous computations and assumptions. STMr package allow users to estimate individual reps-max relationships, implement various progression tables, and create numerous set and rep schemes. The STMr package is originally created as a tool to help writing JovanoviÄ M. (2020) Strength Training Manual <ISBN:979-8604459898>.

This package provides a consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.

An interactive shiny application to assist in determining sample sizes for common survey designs such as simple random sampling', stratified sampling', and cluster sampling'. It includes formulas, helper calculators, and illustrative examples.

This package provides a simple interface to recursively list files from a directory, filter them using a regular expression, read their contents, and extract lines that match a user-defined pattern. The package returns a dataframe containing the matched lines, their line numbers, file paths, and the corresponding matched substrings. Designed for quick code base exploration, log inspection, or any use case involving pattern-based file and line filtering.