Fit Drift Diffusion Model
Skill Verifiziert AktivFit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
To provide a robust and validated method for estimating cognitive parameters from behavioral data, enabling researchers to model binary decision-making processes.
Funktionen
- Parameter estimation for DDM variants
- Model comparison using BIC
- Parameter recovery validation
- Data cleaning and preparation for DDM
- Visual and statistical model fit assessment
Anwendungsfälle
- Modeling binary decisions with reaction time data
- Estimating cognitive parameters like drift rate and boundary separation
- Comparing different sequential sampling models
- Validating the reliability of DDM fitting pipelines
Nicht-Ziele
- Fitting models other than drift-diffusion variants
- Advanced statistical analyses beyond DDM evaluation
- Experimental design for data collection (though related skills exist)
Installation
/plugin install agent-almanac@pjt222-agent-almanacQualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
Survey Insect Population
100Design and execute insect population surveys covering survey design, sampling methods, field execution, specimen identification, diversity index calculation including Shannon-Wiener and Simpson indices, statistical analysis, and reporting. Covers defining survey objectives, selecting study sites, determining sampling intensity and replication, choosing sampling methods appropriate to target taxa, standardizing collection effort, recording environmental covariates, identifying specimens to the lowest practical taxonomic level, calculating species richness, Shannon-Wiener diversity (H'), Simpson diversity (1-D), evenness, rarefaction curves, multivariate ordination, and producing survey reports with species lists and conservation implications. Use when conducting baseline biodiversity assessments, monitoring insect populations over time, comparing insect communities across habitats or treatments, assessing environmental impact, or supporting conservation planning with quantitative ecological data.
Measure Experiment Design
100Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
PyDESeq2
100Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Ops Yolo
100YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltered Hard Truths report. After user types YOLO, autonomously runs the business for a day using /loop.
Pipeline Forecasting
100Generate predictive pipeline forecasts with confidence intervals and scenario modeling for revenue planning
Arize Prompt Optimization
100Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.