Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.

This package provides a supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) <doi:10.1002/sam.11508>. This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646.

Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS) following the method described in Zhou and Stephens. (2014), <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.

This package provides tools for reading and writing biological sequences in multiple formats, including FASTA', PHYLIP', CLUSTAL', STOCKHOLM', MEGA and GenBank'. Supports interleaved and sequential layouts where applicable, converts between formats, and manipulates sequence sets (e.g., filtering by patterns and computing consensus sequences from alignments). Also includes functions to download nucleotide records from NCBI by accession.

Pass named and unnamed character vectors into specified positions in strings. This represents an attempt to replicate some of python's string formatting.

Enables deploying configuration file-based shiny apps with minimal programming for interactive exploration and analysis showcase of molecular expression data. For exploration, supports visualization of correlations between rows of an expression matrix and a table of observations, such as clinical measures, and comparison of changes in expression over time. For showcase, enables visualizing the results of differential expression from package such as limma', co-expression modules from WGCNA and lower dimensional projections.

Implementation of the BLEU-Score in C++ to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.

This package provides a Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.

sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats.

Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solà s, M. (2018) <arXiv:1803.03333>.

This package provides a collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web server space has been provided by Ricardo Energy & Environment.

Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities.

Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.

It provides cumulative distribution function (CDF), quantile, p-value, statistical power calculator and random number generator for a collection of group-testing procedures, including the Higher Criticism tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the one-sided phi-divergence tests, etc. The input are a group of p-values. The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of signal detection, the null hypothesis means no signals. In the context of the goodness-of-fit testing, which contrasts a group of i.i.d. random variables to a given continuous distribution, the input p-values can be obtained by the CDF transformation. The null hypothesis means that these random variables follow the given distribution. For reference, see [1]Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and power of optimal signal-detection statistics in finite case", IEEE Transactions on Signal Processing (2020) 68, 1021-1033; [2] Hong Zhang and Zheyang Wu. "The general goodness-of-fit tests for correlated data", Computational Statistics & Data Analysis (2022) 167, 107379.

An efficient implementation of SCCI using Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.

Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.

Simple and flexible quizzes in shiny'. Easily create quizzes from various pre-built question and choice types or create your own using htmltools and shiny packages as building blocks. Integrates with larger shiny applications. Ideal for non-web-developers such as educators, data scientists, and anyone who wants to assess responses interactively in a small form factor.

Allows to connect selectizeInputs widgets as filters to a reactable table. As known from spreadsheet applications, column filters are interdependent, so each filter only shows the values that are really available at the moment based on the current selection in other filters. Filter values currently not available (and also those being available) can be shown via popovers or tooltips.

Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>, Zellner and Theil (1962) <doi:10.2307/1911287>, and Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.

It allows to quickly perform permutation-based closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) <arXiv:2102.11759>.

R version of scperturb tool for single-cell perturbation analysis. Contains wrappers for performing E-statistics for Seurat objects. More details on the method can be found in Peidli et al. (2023) <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) <https://orbilu.uni.lu/handle/10993/63425>.