Algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data sets, estimation of positions,comparison of the performance of different models, and graphical visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or probabilistic fingerprinting are implemented in this package to perform analysis and estimations over RSSI data sets.

An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004>.

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources: (1) pediatric EHR data, (2) EHR data from the general patient population, and (3) existing hierarchical medical ontology knowledge shared across different patient populations. See Li et al. (2024) <doi:10.1038/s41746-024-01320-4> for details.

This package provides an R wrapper for the MD4C (Markdown for C') library. Functions exist for parsing markdown ('CommonMark compliant) along with support for other common markdown extensions (e.g. GitHub flavored markdown, LaTeX equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

This package provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well.

Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.

Bayesian trophic position models using stan by leveraging brms for stable isotope data. Trophic position models are derived by using equations from Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>, Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, and Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.

Analyze Peptide Array Data and characterize peptide sequence space. Allows for high level visualization of global signal, Quality control based on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters, Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU signal weighting. Basic signal trends can be used to generate peptides that follow the observed compositional trends.

qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples.

Least Angle Regression ("LAR") is a model selection algorithm; a useful and less greedy version of traditional forward selection methods. A simple modification of the LAR algorithm implements Tibshirani's Lasso; the Lasso modification of LARS calculates the entire Lasso path of coefficients for a given problem at the cost of a single least squares fit. Another LARS modification efficiently implements epsilon Forward Stagewise linear regression.

Enables binary package installations on Linux distributions. Provides functions to manage packages via the distribution's package manager. Also provides transparent integration with R's install.packages() and a fallback mechanism. When installed as a system package, interacts with the system's package manager without requiring administrative privileges via an integrated D-Bus service; otherwise, uses sudo. Currently, the following backends are supported: DNF, APT, ALPM.

This package provides a shortcut procedure is proposed to implement closed testing for large-scale multiple testings, especially with the global test. This shortcut is asymptotically equivalent to closed testing and post hoc. Users could detect any possible sets of features or pathways with family-wise error rate controlled. The global test is powerful to detect associations between a group of features and an outcome of interest.

This package provides a revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. The package also contains cvm.test(), for doing one-sample Cramer-von Mises goodness-of-fit tests.

Wrapper functions that interface with FSL <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used neuroimaging software, using system commands. The goal is to be able to interface with FSL completely in R, where you pass R objects of class nifti', implemented by package oro.nifti', and the function executes an FSL command and returns an R object of class nifti if desired.

Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package vegan') and PCA (package FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y RÃ os (1795) <https://books.google.cat/books?id=030t0OqlX2AC> (In Spanish).

Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.

Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to svmlight and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing.

Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions. Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the Eigen library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.

Test whether equality and order constraints hold for all individuals simultaneously by comparing Bayesian mixed models through Bayes factors. A tutorial style vignette and a quickstart guide are available, via vignette("manual", "quid"), and vignette("quickstart", "quid") respectively. See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.

This package creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.