This module allows libraries to have a dependency to a small module instead of the full Log-Report distribution. The full power of Log::Report is only released when the main program uses that module. In that case, the module using the Optional will also use the full Log::Report, otherwise the dressed-down Log::Report::Minimal version.

This small package modifies the BibLaTeX macro which reads a .bbl file created by Biber. It is thus possible to include a .bbl file into the main document with the environment and send it to a publisher who does not need to run the Biber program. However, when the bibliography changes one has to create a new .bbl file.

This package provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.

Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package is for learning purposes and allows users to optimize various functions or parameters by mimicking biological evolution processes such as selection, crossover, and mutation. Ideal for tasks like machine learning parameter tuning, mathematical function optimization, and solving an optimization problem that involves finding the best solution in a discrete space.

U-Boot is a bootloader used mostly for ARM boards. It also initializes the boards (RAM etc).

It allows network booting and uses the device-tree from the firmware, allowing the usage of overlays. It can act as an EFI firmware for the grub-efi-netboot-removable-bootloader. This is a 32-bit build of U-Boot.

This package only contains the file u-boot.bin.

U-Boot is a bootloader used mostly for ARM boards. It also initializes the boards (RAM etc).

It allows network booting and uses the device-tree from the firmware, allowing the usage of overlays. It can act as an EFI firmware for the grub-efi-netboot-removable-bootloader. This is a 32-bit build of U-Boot.

This package only contains the file u-boot.bin.

The curatedMetagenomicData package provides standardized, curated human microbiome data for novel analyses. It includes gene families, marker abundance, marker presence, pathway abundance, pathway coverage, and relative abundance for samples collected from different body sites. The bacterial, fungal, and archaeal taxonomic abundances for each sample were calculated with MetaPhlAn3, and metabolic functional potential was calculated with HUMAnN3. The manually curated sample metadata and standardized metagenomic data are available as (Tree)SummarizedExperiment objects.

Utilize the shiny interface for visualizing results from a pyDarwin (<https://certara.github.io/pyDarwin/>) machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models, allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an R Markdown document and rendered in various output formats.

This package provides a user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

U-Boot is a bootloader used mostly for ARM boards. It also initializes the boards (RAM etc).

It allows network booting and uses the device-tree from the firmware, allowing the usage of overlays. It can act as an EFI firmware for the grub-efi-netboot-removable-bootloader. This is a common 64-bit build of U-Boot for all 64-bit capable Raspberry Pi variants.

This package only contains the file u-boot.bin.

This package provides a plug-in for R Commander ('Rcmdr'). The package is a Graphical User Interface (GUI) in which several biclustering methods can be executed, followed by diagnostics and plots of the results. Further, the GUI also has the possibility to connect the methods to more general diagnostic packages for biclustering. Biclustering methods from biclust', fabia', s4vd', iBBiG', isa2', BiBitR', rqubic and BicARE are implemented. Additionally, superbiclust and BcDiag are also implemented to be able to further investigate results. The GUI also provides a couple of extra utilities to export, save, search through and plot the results. RcmdrPlugin.BiclustGUI also provides a very specific framework for biclustering in which new methods, diagnostics and plots can be added. Scripts were prepared so that R-package developers can freely design their own dialogs in the GUI which can then be added by the maintainer of RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of tcltk and Rcmdr and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').).

This package provides some tabulated data to be be referred to in a discussion in a vignette accompanying my upcoming R package playWholeHandDriverPassParams'. In addition to that specific purpose, these may also provide data and illustrate some computational approaches that are relevant to card games like hearts or bridge.This package refers to authentic data from Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>.

Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.

Analyzes and modifies metabolomics raw data (generated using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or an Isotopic-Ratio approach. Quality control plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.

This package provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.

This package provides a client library for Vipul's Razor. Vipul's Razor is a distributed, collaborative, spam detection and filtering network. Through user contribution, Razor establishes a distributed and constantly updating catalogue of spam in propagation that is consulted by email clients to filter out known spam. Detection is done with statistical and randomized signatures that efficiently spot mutating spam content. User input is validated through reputation assignments based on consensus on report and revoke assertions which in turn is used for computing confidence values associated with individual signatures.

This package provides a brotli decompressor that with an interface avoiding the rust stdlib. This makes it suitable for embedded devices and kernels. It is designed with a pluggable allocator so that the standard lib's allocator may be employed. The default build also includes a stdlib allocator and stream interface. Disable this with --features=no-stdlib. Alternatively, --features=unsafe turns off array bounds checks and memory initialization but provides a safe interface for the caller. Without adding the --features=unsafe argument, all included code is safe. For compression in addition to this library, download https://github.com/dropbox/rust-brotli.

This package provides functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978â 0â 387â 72578â 9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

This package implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.

Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.

DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) ambiguous reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).

Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions.

This package provides functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. EHRtemporalVariability is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. EHRtemporalVariability allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional EHRtemporalVariability Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding. (Sáez et al. 2020) <doi:10.1093/gigascience/giaa079>.