「estimate」の検索結果

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

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A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

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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.

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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con

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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

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Fetch website traffic estimates (monthly visits, traffic sources, top countries, keywords, engagement, ranks) for any domain from SimilarWeb. Use when the user asks about a domain's traffic, monthly visits, traffic sources, audience countries, or wants to compare/benchmark sites against competitors.

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Calculate realistic affiliate earnings projections before committing to a program. Use this skill when the user asks about affiliate earnings, projecting income, calculating commissions, estimating how much they can make, comparing program payouts, or says "how much can I make promoting X", "calculate my affiliate income", "is this commission worth it", "how long to first $1000", "compare earnings between programs", "traffic to income calculator", "what conversion rate should I expect", "earnings estimate for affiliate program", "how many sales do I need".

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Weekly on a Friday, conditional browser lane. Scores the week from the ledgers with a source beside every number and an estimate nowhere, reads the review and rating movement off the member's own listing screens, replays the browser flows the weekday routines depend on, and names the one product change that would have removed the most tickets this week. It sends only where you released the channel, never posts, never resolves anything, spends only where you released it, and never touches a credential.

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Use when a session ends and the user wants a summary of what was done and what it cost. Writes a shareable SESSION_REPORT.md with outcomes, work performed and estimated resource usage.

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Show real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.

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Inspect LiveView socket assigns for memory bloat — missing temporary_assigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporary_assigns.

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Inspect LiveView socket assigns for memory bloat — missing temporary_assigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporary_assigns.

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Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub.

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Estimate and communicate business impact of insights. Use when sizing opportunities discovered in analysis, calculating ROI of recommended actions, or prioritizing initiatives by potential impact.

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Trigger for: writing, revising, converting, or descoping a federal Statement of Work, Performance Work Statement, SOW, PWS, or SOO; develop executable requirements; define contract scope; create measurable performance standards; or prepare requirements before an IGCE. Produce a contract-file-ready .docx plus separate chat-only staffing and Section B handoffs. Never place FTEs, SOC codes, staffing estimates, IGCE content, CLINs, or pricing schedules inside the SOW/PWS body. Do NOT use for the IGCE, market research report, acquisition plan, QASP, or clause-selection matrix.

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Trigger for: OT project description, OTA scope, Research OT under 10 U.S.C. 4021, Prototype OT under 10 U.S.C. 4022, follow-on production scope under 10 U.S.C. 4022(f), milestone-based project scope, prototype objective, phase or go/no-go structure, BAA white-paper conversion, SOO conversion, transition planning, or scope reduction before OT cost analysis. Produce a milestone-based .docx plus a separate chat-only handoff to OT Cost Analysis. Use TRL only when the project supports it. Never place cost estimates, funding profiles, contribution calculations, prices, or skill-routing content in the document. Do NOT use for a FAR SOW/PWS, OT cost analysis, IGCE, agreement terms and conditions, or an Agreements Officer's authority, participant-status, successful-completion, or follow-on determination.

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Get upcoming earnings dates with timing (before/after market) and EPS estimates. Use when user asks about earnings dates, earnings calendar, when a company reports, or upcoming earnings.

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When the user needs to estimate market size, understand market dynamics, or validate that a market opportunity is large enough to pursue.

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Use when your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened

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Comprehensive verification system for construction automation deliverables. Use after completing estimates, schedules, reports, or data processing tasks to ensure quality.

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Validate construction data inputs before processing: cost estimates, schedules, BIM data, field reports. Catch errors early with domain-specific rules.

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Analyze construction budget variances. Compare estimated vs actual costs, identify trends, forecast final costs, and generate variance reports for cost control.

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Excel/spreadsheet processing for construction: estimates, schedules, tracking logs, quantity takeoffs. Formulas, formatting, analysis.

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Calculate carbon footprint of construction projects. Estimate CO2 emissions from materials, transportation, and construction processes using emission factors databases.

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