「conductor」の検索結果

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o

日本語の概要は準備中です。原文の説明を表示しています。

This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enric

日本語の概要は準備中です。原文の説明を表示しています。

The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and enc

日本語の概要は準備中です。原文の説明を表示しています。

In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio

日本語の概要は準備中です。原文の説明を表示しています。

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.

日本語の概要は準備中です。原文の説明を表示しています。

Tools to analyze & visualize Illumina Infinium methylation arrays.

日本語の概要は準備中です。原文の説明を表示しています。

Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.

日本語の概要は準備中です。原文の説明を表示しています。

The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3.

日本語の概要は準備中です。原文の説明を表示しています。

This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.

日本語の概要は準備中です。原文の説明を表示しています。

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

日本語の概要は準備中です。原文の説明を表示しています。

Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements

日本語の概要は準備中です。原文の説明を表示しています。

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

日本語の概要は準備中です。原文の説明を表示しています。

Infrastructure shared by all the Biostrings-based genome data packages.

日本語の概要は準備中です。原文の説明を表示しています。

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false

日本語の概要は準備中です。原文の説明を表示しています。

This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract biclus

日本語の概要は準備中です。原文の説明を表示しています。

Create, edit, evaluate, and package agent skills. Use when building a new skill from scratch, improving an existing skill, fixing a skill that never triggers or fires unreliably, running evals to test a skill, benchmarking skill performance, optimizing a skill's description, reviewing third-party skills for quality, or packaging skills for distribution — even if the user doesn't explicitly say "skill" (e.g. "teach Claude to do X", "make the agent always follow Y"). Not for using skills or general coding tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Electrical power distribution design for infrastructure: load calculations (NEC 220), conductor sizing (NEC 310.16), transformer/UPS/PDU selection, redundancy architectures (N through 2N+1), DC distribution, and voltage classes 120V-480V. Activates for electrical load calculations, conductor sizing, power equipment selection, data center power design, and redundancy architecture planning.

日本語の概要は準備中です。原文の説明を表示しています。

Decision matrix for selecting multi-agent topologies (Supervisor, Swarm, Hierarchical, Conductor) with token economics, failure modes, and escalation paths

日本語の概要は準備中です。原文の説明を表示しています。

Generate hand-crafted web animation patterns as self-contained HTML: typewriter/rotator effects, progress bar simulations, file review state machines, staggered reveals, terminal status displays, streaming AI-response demos, popover/dialog/toast lifecycles, Lottie orchestration, scroll-driven sequences. Behavioral animations that simulate live software. Triggers on typewriter effect, progress animation, stagger reveal, terminal animation, streaming text, AI demo animation, toast animation, data simulation, product demo animation, loading sequence, conductor motion.

日本語の概要は準備中です。原文の説明を表示しています。

Install bioinformatics tools for ENCODE data analysis. Covers CLI tools (BWA, STAR, samtools, MACS2), R/Bioconductor packages (DESeq2, Seurat, ChIPseeker), Python packages (Scanpy, deeptools), and Nextflow pipeline infrastructure. Generates conda environments, R install scripts, and Python requirements. Use when the user needs to set up a bioinformatics workstation, install tools for a specific assay, create reproducible environments, or troubleshoot dependency issues. Trigger on: install tools, set up environment, conda create, bioinformatics setup, install R packages, install Bioconductor, install pipeline tools.

日本語の概要は準備中です。原文の説明を表示しています。

Configure a Rails project to work with Conductor (parallel coding agents)

日本語の概要は準備中です。原文の説明を表示しています。

Validates Conductor project artifacts for completeness, consistency, and correctness. Use after setup, when diagnosing issues, or before implementation to verify project context.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations.

日本語の概要は準備中です。原文の説明を表示しています。

Creates and maintains project context artifacts (product.md, tech-stack.md, workflow.md, tracks.md) in a `conductor/` directory. Scaffolds new projects from scratch, extracts context from existing codebases, validates artifact consistency before implementation, and synchronizes documents as the project evolves. Use when setting up a project, creating or updating product docs, managing a tech stack file, defining development workflows, tracking work units, onboarding to an existing codebase, or running project scaffolding.

日本語の概要は準備中です。原文の説明を表示しています。