minor

README.md

@@ -1,4 +1,4 @@
-# Cognitive Models with Julia
+# Cognitive Models with Julia <img src='content/median/cover.png' align="right" height="200" />
 
 [![](https://img.shields.io/badge/status-looking_for_collaborators-orange)](https://github.com/DominiqueMakowski/CognitiveModels/issues)
 [![](https://img.shields.io/badge/access-open-green)](https://dominiquemakowski.github.io/CognitiveModels/)

content/.jupyter_cache/executed/4a8dce518c228fb3cf99db4583eedbb4/base.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cef1b013",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import IJulia\n",
+    "\n",
+    "# The julia kernel has built in support for Revise.jl, so this is the \n",
+    "# recommended approach for long-running sessions:\n",
+    "# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n",
+    "\n",
+    "# Users should enable revise within .julia/config/startup_ijulia.jl:\n",
+    "# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n",
+    "\n",
+    "# clear console history\n",
+    "IJulia.clear_history()\n",
+    "\n",
+    "fig_width = 7\n",
+    "fig_height = 5\n",
+    "fig_format = :retina\n",
+    "fig_dpi = 96\n",
+    "\n",
+    "# no retina format type, use svg for high quality type/marks\n",
+    "if fig_format == :retina\n",
+    "  fig_format = :svg\n",
+    "elseif fig_format == :pdf\n",
+    "  fig_dpi = 96\n",
+    "  # Enable PDF support for IJulia\n",
+    "  IJulia.register_mime(MIME(\"application/pdf\"))\n",
+    "end\n",
+    "\n",
+    "# convert inches to pixels\n",
+    "fig_width = fig_width * fig_dpi\n",
+    "fig_height = fig_height * fig_dpi\n",
+    "\n",
+    "# Intialize Plots w/ default fig width/height\n",
+    "try\n",
+    "  import Plots\n",
+    "\n",
+    "  # Plots.jl doesn't support PDF output for versions < 1.28.1\n",
+    "  # so use png (if the DPI remains the default of 300 then set to 96)\n",
+    "  if (Plots._current_plots_version < v\"1.28.1\") & (fig_format == :pdf)\n",
+    "    Plots.gr(size=(fig_width, fig_height), fmt = :png, dpi = fig_dpi)\n",
+    "  else\n",
+    "    Plots.gr(size=(fig_width, fig_height), fmt = fig_format, dpi = fig_dpi)\n",
+    "  end\n",
+    "catch e\n",
+    "  # @warn \"Plots init\" exception=(e, catch_backtrace())\n",
+    "end\n",
+    "\n",
+    "# Initialize CairoMakie with default fig width/height\n",
+    "try\n",
+    "  import CairoMakie\n",
+    "\n",
+    "  # CairoMakie's display() in PDF format opens an interactive window\n",
+    "  # instead of saving to the ipynb file, so we don't do that.\n",
+    "  # https://github.com/quarto-dev/quarto-cli/issues/7548\n",
+    "  if fig_format == :pdf\n",
+    "    CairoMakie.activate!(type = \"png\")\n",
+    "  else\n",
+    "    CairoMakie.activate!(type = string(fig_format))\n",
+    "  end\n",
+    "  CairoMakie.update_theme!(resolution=(fig_width, fig_height))\n",
+    "catch e\n",
+    "    # @warn \"CairoMakie init\" exception=(e, catch_backtrace())\n",
+    "end\n",
+    "  \n",
+    "# Set run_path if specified\n",
+    "try\n",
+    "  run_path = raw\"C:\\Users\\domma\\Dropbox\\Software\\CognitiveModels\\content\"\n",
+    "  if !isempty(run_path)\n",
+    "    cd(run_path)\n",
+    "  end\n",
+    "catch e\n",
+    "  @warn \"Run path init:\" exception=(e, catch_backtrace())\n",
+    "end\n",
+    "\n",
+    "\n",
+    "# emulate old Pkg.installed beahvior, see\n",
+    "# https://discourse.julialang.org/t/how-to-use-pkg-dependencies-instead-of-pkg-installed/36416/9\n",
+    "import Pkg\n",
+    "function isinstalled(pkg::String)\n",
+    "  any(x -> x.name == pkg && x.is_direct_dep, values(Pkg.dependencies()))\n",
+    "end\n",
+    "\n",
+    "# ojs_define\n",
+    "if isinstalled(\"JSON\") && isinstalled(\"DataFrames\")\n",
+    "  import JSON, DataFrames\n",
+    "  global function ojs_define(; kwargs...)\n",
+    "    convert(x) = x\n",
+    "    convert(x::DataFrames.AbstractDataFrame) = Tables.rows(x)\n",
+    "    content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => convert(v)) for (k, v) in kwargs])\n",
+    "    tag = \"<script type='ojs-define'>$(JSON.json(content))</script>\"\n",
+    "    IJulia.display(MIME(\"text/html\"), tag)\n",
+    "  end\n",
+    "elseif isinstalled(\"JSON\")\n",
+    "  import JSON\n",
+    "  global function ojs_define(; kwargs...)\n",
+    "    content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => v) for (k, v) in kwargs])\n",
+    "    tag = \"<script type='ojs-define'>$(JSON.json(content))</script>\"\n",
+    "    IJulia.display(MIME(\"text/html\"), tag)\n",
+    "  end\n",
+    "else\n",
+    "  global function ojs_define(; kwargs...)\n",
+    "    @warn \"JSON package not available. Please install the JSON.jl package to use ojs_define.\"\n",
+    "  end\n",
+    "end\n",
+    "\n",
+    "\n",
+    "# don't return kernel dependencies (b/c Revise should take care of dependencies)\n",
+    "nothing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53bd139d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using Distributions\n",
+    "\n",
+    "# Reparameterized Beta distribution\n",
+    "function BetaMod(μ, σ)\n",
+    "    α = μ * (μ * (1 - μ) / σ^2 - 1)\n",
+    "    β = α * (1 - μ) / μ\n",
+    "    return Beta(α, β)\n",
+    "end\n",
+    "\n",
+    "\n",
+    "\n",
+    "mean(BetaMod(0.5, 0.0833))\n",
+    "var(BetaMod(0.2, 0.1))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.10.2",
+   "language": "julia",
+   "name": "julia-1.10"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

content/.quarto/_freeze/3_scales/execute-results/html.json

@@ -0,0 +1,12 @@
+{
+  "hash": "b7153c2b346aaba747f06dca75c74302",
+  "result": {
+    "engine": "jupyter",
+    "markdown": "# Choice and Scales\n\n![](https://img.shields.io/badge/status-not_started-red)\n\n1. Beta models \n\n::: {#53bd139d .cell execution_count=1}\n``` {.julia .cell-code}\nusing Distributions\n\n# Reparameterized Beta distribution\nfunction BetaMod(μ, σ)\n    α = μ * (μ * (1 - μ) / σ^2 - 1)\n    β = α * (1 - μ) / μ\n    return Beta(α, β)\nend\n\n\n\nmean(BetaMod(0.5, 0.0833))\nvar(BetaMod(0.2, 0.1))\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\n0.01\n```\n:::\n:::\n\n\n2. OrdBeta models for slider scales\n3. Logistic models for binary data\n\nUse the speed accuracy data that we use in the next chapter.\n\n",
+    "supporting": [
+      "3_scales_files"
+    ],
+    "filters": [],
+    "includes": {}
+  }
+}

content/.quarto/cites/index.json

@@ -1 +1 @@
-{"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"2_predictors.qmd":[],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"],"1_introduction.qmd":[],"3_scales.qmd":[],"index.qmd":[],"references.qmd":[],"4a_rt_descriptive.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"5_individual.qmd":[],"4b_rt_generative.qmd":[]}
+{"4a_rt_descriptive.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","wagenmakers2005relation","limpert2001log","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"5_individual.qmd":[],"2_predictors.qmd":[],"3_scales.qmd":[],"references.qmd":[],"1_introduction.qmd":[],"index.qmd":[],"4b_rt_generative.qmd":[],"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"]}