Quantile regression (QR) for Linear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

This package implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

The best way to implement middle ware for shiny Applications. tower is designed to make implementing behavior on top of shiny easy with a layering model for incoming HTTP requests and server sessions. tower is a very minimal package with little overhead, it is mainly meant for other package developers to implement new behavior.

Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank.

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.

For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.

Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements.

Redkite is a small GUI toolkit developed in C++17 and inspired from other well known GUI toolkits such as Qt and GTK. It is minimal on purpose and is intended to be statically linked to applications, therefore satisfying any requirements they may have to be self contained, as is the case with audio plugins.

The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.

This package provides functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. It includes functions for rudimentary data cleaning, construction and summarization of correlation networks, module identification and functions for relating both variables and modules to sample traits. It also includes a number of utility functions for data manipulation and visualization.

This package reads and writes data files like CSV, TSV and FWF. When reading it uses a quick initial indexing step, then reads the values lazily, so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.

This package allows the user to create new Github gists, update gists with new files, rename files, delete files, get and delete gists, star and un-star them, fork them, open a gist in your default browser, get an embed code for a gist, list gist commits, and get rate limit information when authenticated.

This package provides a proof of concept implementation of regularized non-negative matrix factorization optimization. A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well as an additive update derived from that multiplicative update. See also Pav (2004) <doi:10.48550/arXiv.2410.22698>.

This package performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

This package contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.

Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2023+) <arXiv:2202.12989>.

You can use this function to easily draw a combined histogram and restricted cubic spline. The function draws the graph through ggplot2'. RCS fitting requires the use of the rcs() function of the rms package. Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.

This package provides a group of sample points are evaluated against a user-defined expression, the sample points are lists of parameters with values that may be substituted into that expression. The genetic algorithm attempts to make the result of the expression as low as possible (usually this would be the sum of residuals squared).

Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and HTML output, color ramp and palette manipulation, Rmarkdown cache import, styled Excel worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools.

This package provides a shiny application for forensic kinship testing, based on the pedsuite R packages. KLINK is closely aligned with the (non-R) software Familias and FamLink', but offers several unique features, including visualisations and automated report generation. The calculation of likelihood ratios supports pairs of linked markers, and all common mutation models.

Efficient procedures for fitting the regularization path for linear, binomial, multinomial, Ising and Potts models with lasso, group lasso or column lasso(only for multinomial) penalty. The package uses Linearized Bregman Algorithm to solve the regularization path through iterations. Bregman Inverse Scale Space Differential Inclusion solver is also provided for linear model with lasso penalty.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.

Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022) <doi:10.48550/arXiv.2209.07270>). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables.

The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm(), glm() and associated summaries from the package stats'.