This package provides fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen glue.

This package provides an SCSS compiler, powered by the libsass library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the Sass CSS extension language, which is stable, powerful, and CSS compatible.

Recoll finds documents based on their contents as well as their file names. It can search most document formats, but you may need external applications for text extraction. It can reach any storage place: files, archive members, email attachments, transparently handling decompression.

This package provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.

This package implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Convert several png files into an animated png file. This package exports only a single function `apng'. Call the apng function with a vector of file names (which should be png files) to convert them to a single animated png file.

This package provides functions for summarizing and plotting the output of the command-line tool BeXY (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.

Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

This package provides tools to extract information from the Intergovernmental Organizations ('IGO') Database , version 3, provided by the Correlates of War Project <https://correlatesofwar.org/>. See also Pevehouse, J. C. et al. (2020). Version 3 includes information from 1815 to 2014.

Use R to make requests to the US Census Bureau's International Data Base API. Results are returned as R data frames. For more information about the IDB API, visit <https://www.census.gov/data/developers/data-sets/international-database.html>.

Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in C++'. It uses Rcpp', Armadillo and GSL to maximize computation efficiency.

An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.

Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.

Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of mbir is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.

This package provides a novel mediation analysis approach to address zero-inflated mediators containing true zeros and false zeros. See Jiang et al (2023) "A Novel Causal Mediation Analysis Approach for Zero-Inflated Mediators" <arXiv:2301.10064> for more details.

This package creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.

Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).

Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the nlme package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".

Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.

Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2022) <doi:10.1111/sjos.12580>. Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package.

Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.

This package provides functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.