An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.

This package adds new RSS generation options to the org-publish-project-alist variable (see the Org manual if you are new to the publishing options). It adds :auto-rss and other options that work similar to the included :auto-sitemap functionality. This should make it easy for users to add RSS feeds to existing Org-based websites.

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>.

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.

info-rename-buffer-mode is a global minor-mode that automatically renames Info buffers to match their visiting manual. That's a useful feature when consulting several Info manuals simultaneously, because it frees the user from the burden of renaming Info buffers to descriptive names manually before visiting another manual, thus avoiding accidentally overriding the currently visited node in case the user tries to open a new Info buffer.

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.

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.

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 implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for economic disaggregation represents an original methodological contribution.

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>.

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.

Convenient download functions enabling access Open Source Asset Pricing (OpenAP) data. This package enables users to download predictor portfolio returns (over 200 cross-sectional predictors with multiple portfolio construction methods) and firm characteristics (over 200 characteristics replicated from the academic asset pricing literature). Center for Research in Security Prices (CRSP)-based variables such as Price, Size, and Short-term Reversal can be downloaded with a Wharton Research Data Services (WRDS, <https://wrds-www.wharton.upenn.edu/>) subscription. For a full list of what is available, see <https://www.openassetpricing.com/>.

Supports propensity score-based methodsâ including matching, stratification, and weightingâ for estimating causal treatment effects. It also implements calibration using negative control outcomes to enhance robustness. debiasedTrialEmulation facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methodsâ including approaches to address immortal time bias, information bias, selection bias, and informative censoringâ are under development. Users interested in these extended features are encouraged to contact the package authors.

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>.