### A Pluto.jl notebook ###
# v0.19.3

using Markdown
using InteractiveUtils

# ╔═╡ 7ac0c689-419a-428b-9006-69d3e6c1ea7e
using PlutoUI; TableOfContents(title = "目录", aside = false, depth = 4)

# ╔═╡ d0dfdb8d-2b7a-4956-802d-73922db32330
begin
	using DataFrames, CSV
	using FreqTables
	using Statistics: mean, std
	using GLM
	using CategoricalArrays
end

# ╔═╡ b36a8b22-992c-4b50-a8ef-23852f73e26c
let
	using CairoMakie #GLMakie
	set_theme!(theme_minimal())
	update_theme!(
		backgroundcolor = RGBf(0.98, 0.98, 0.98),
		resolution = (1024, 768)
	 )
end

# ╔═╡ e8a9ae4d-438a-4496-b75c-ee9a5e91d740
md"""
### 载入包
"""

# ╔═╡ b7a11e97-fba8-4656-b529-ff6f21a75a0f
md"""
### 实验一
"""

# ╔═╡ ab814eb8-b384-40d0-95b2-139031baaa35
md"""
#### 读入数据
"""

# ╔═╡ 3e149cf5-4387-4a05-a13c-4b00167965b5
md"""
- trialType: complete表示完全反馈条件； partial表示部分反馈条件
- x1，y1：左边转盘的两个分数
- p，p2：x1的概率为p1；y1的概率为p2
- x2，y2：右边转盘的两个分数
- q，q2：x2的概率为q1；y2的概率为q2
- left points：选择左边转盘得到的分数
- right points：选择右边转盘得到的分数
- LeftYourUn：选择左边转盘没有得到的分数
- RightYouUn：选择右边转盘没有得到的分数
- `key_resp_4.keys`：被试选择了左边转盘（left）还是右边转盘（right）。因此从key_resp_4.keys和left  points、right points就能看出被试这轮得到的分数。
- rating：被试的情绪评分
- total score：被试在转盘上累计的总分，也可以通过下一轮的总分减去上一轮的总分得出被试上一轮得到的分数。
"""

# ╔═╡ 2a9d311c-a766-4f32-ac8b-64a9b63cedcc
csv_list1 = filter(endswith(".csv"), readdir("data/Experiment_1", join = true));

# ╔═╡ 7235b483-8484-4ca2-a700-2dbea497ac26
df1 = mapreduce(vcat, csv_list1[1:41]) do csv
	CSV.read(csv, DataFrame, stringtype = String,
			 drop = (i, name) -> startswith(string(name), "Column")
	)
end;

# ╔═╡ 91006353-4ea7-4fd0-98c2-f23e6a38a351
dropmissing!(df1, [Symbol("key_resp_4.keys"), :leftPoints, :rightPoints]);

# ╔═╡ 06e153e3-f076-4e16-8ab6-141cc2b0d7bb
function neg2pos2(key, left, right)
	if key == "left"
		if left + right < 0
			return (OwnScore = left + 200, OtherScore = right + 200, Gain = "Lose")
		elseif left + right >= 0
			return (OwnScore = left,       OtherScore = right,       Gain = "Gain")
		end
	elseif key == "right"
		if left + right < 0
			return (OwnScore = right + 200, OtherScore = left + 200, Gain = "Lose")
		elseif left + right >= 0
			return (OwnScore = right,       OtherScore = left,       Gain = "Gain")
		end
	end
end

# ╔═╡ 30ad6818-714b-4362-b8b4-af193dcd70cf
transform!(df1, [Symbol("key_resp_4.keys"), :leftPoints, :rightPoints] => 
		ByRow(neg2pos2) => AsTable
);

# ╔═╡ 401c49d8-4096-480f-8271-5e37e9a307f5
select!(df1, 
	["participant", "trialType", "OwnScore", "OtherScore", "Gain", "rating"]
);

# ╔═╡ 80ad622e-0783-4b7c-942d-73adb7eff2e8
rename!(df1, "participant" => "Participant",  
	"trialType" => "Feedback", "rating" => "Emotion");

# ╔═╡ 1ba1c644-a1d9-4233-9085-3ac871d95a9e
md"""
#### 总体结果
"""

# ╔═╡ 9c8b0137-d0af-4a6e-b2ef-28d30bd80c28
df1c = combine(
	groupby(df1, [:Participant, :Feedback, :Gain]), 
	:Emotion => mean => :Emotion
);

# ╔═╡ 436f5420-3ef0-4a9c-8fc7-b20ea714a889
e1fm1 = lm(@formula(Emotion ~ Feedback * Gain), df1c);

# ╔═╡ e5d58b40-7c37-43c8-a83a-5127b9b0ea38
e1fm2 = lm(@formula(Emotion ~ Feedback + Gain), df1c);

# ╔═╡ a1e0d71c-01a4-42bd-af08-993369cd74b9
e1fm3 = lm(@formula(Emotion ~ Feedback), df1c);

# ╔═╡ 83dfce03-65c2-4d0e-989d-5da22471a024
e1fm4 = lm(@formula(Emotion ~ Gain), df1c);

# ╔═╡ b14a6b03-768d-4bac-8dab-96db011a2acd
ftest(e1fm1.model, e1fm2.model)

# ╔═╡ a61067b2-83b9-4dbb-aa5f-94aaa399ac43
coeftable(e1fm2)

# ╔═╡ 12d7c32f-4a62-4cae-a9b2-39ef597bd084
let
	replace!(df1c.Gain,     "Gain"     => "获益框架", "Lose"    => "损失框架");
	replace!(df1c.Feedback, "complete" => "完全反馈", "partial" => "不完全反馈");
	transform!(df1c,
		:Feedback => (x -> categorical(x, ordered = true)) => :Feedback
	)
	transform!(df1c,
		:Gain => (x -> categorical(x, ordered = true)) => :Gain
	)
	Gain = unique(df1c.Gain)
	Feedback = unique(df1c.Feedback)
	
	fig = Figure()
	for (id1, gg) in enumerate(Gain)
		ax = Axis(fig[1, id1], 
			title = gg, xlabel = "反馈类型", ylabel = "被试情绪自评")
		ylims!(-20, 40)
		dt = df1c[df1c.Gain .== gg, :]
		for (id2, fd) in enumerate(Feedback)
			dtt = dt[dt.Feedback .== fd, :]
			xx = Int.(dtt.Feedback.refs)
			yy = dtt.Emotion
			boxplot!(ax, xx, yy, width = 1, label = fd, whiskerwidth = 0.5)
		end
		ax.xticks = (1:length(levels(Feedback)), levels(Feedback))
		id1 > 1 && hideydecorations!(ax)
	end
	fig
end

# ╔═╡ 76d4e45d-225e-4c4c-9f5e-3f30db7c53d9
md"""
#### 被试得分
"""

# ╔═╡ 1abbbc4d-5213-4632-9f3f-f45ad638b015
df1d = combine(
	groupby(df1, [:Participant, :Feedback, :Gain, :OwnScore]), 
	:Emotion => mean => :Emotion
);

# ╔═╡ 5bd3d6c1-2187-4d24-b354-244ffcb9d41b
e1fm51 = lm(@formula(Emotion ~ Feedback * Gain * OwnScore), df1d);

# ╔═╡ 6b4a960b-205c-4e92-bc7e-ac4d36728316
e1fm52 = lm(@formula(Emotion ~ Feedback + Gain + OwnScore), df1d);

# ╔═╡ bda05cb8-6e31-4c5d-a0df-7b0eadefb1ce
ftest(e1fm51.model, e1fm52.model)

# ╔═╡ d0178ff1-4562-4be2-84d1-5f73ff561c91
coeftable(e1fm52)

# ╔═╡ 4b7d5041-7657-410b-abd6-2cbcb3ae7eb8
let
	replace!(df1d.Gain,     "Gain"     => "获益框架", "Lose"    => "损失框架");
	replace!(df1d.Feedback, "complete" => "完全反馈", "partial" => "不完全反馈");
	fig = Figure()
	for (id1, ff) in enumerate(unique(df1d.Feedback))
		dtt = df1d[df1d.Feedback .== ff, :]
		ax = Axis(fig[1, id1], title = ff,
			xlabel = "被试得分", ylabel = "被试情绪自评", 
			xticks = -200:50:200, yticks = -40:20:40)
		for (id2, gg) in enumerate(unique(df1d.Gain))
			dt = dtt[dtt.Gain .== gg, :]
			xx = dt.OwnScore .+ (iseven(id2) ? 1 : -1) * 6.5
			yy = dt.Emotion
			boxplot!(ax, xx, yy, width = 15, label = gg, whiskerwidth = 0.5)
		end
		id1 > 1 && hideydecorations!(ax)
	end
	axislegend("框架", position = :rb)
	fig
end

# ╔═╡ 396cbce1-4ab7-44f6-ae14-5ae041bf487a
md"""
### 实验二
"""

# ╔═╡ 29c07409-5d62-42aa-a002-b14dfd7c1685
md"""
#### 读入数据
"""

# ╔═╡ 3223d964-3424-4a91-87cd-690876844b4d
md"""
相关说明
- `mychoice`: 被试本试次得分
- `otherchoice`： 电脑本试次得分
- `stay`: 剩余箱子得分
- `key_resp_4.keys`： 电脑先选， 被试按键
- `key_resp_6.keys`： 被试先选， 被试按键
- `rating`: 情绪评分
- `choicepos`: 电脑先选条件下， 电脑选择的位置
"""

# ╔═╡ af32e1d2-b575-11ec-34f6-25b42c355184
cd(@__DIR__) # pwd()

# ╔═╡ 0f5bd14c-638a-4eb1-85e5-729767c5fc4f
md"""
#### 总体结果
"""

# ╔═╡ d5967d18-62d7-4870-b3b7-af7416acb2b4
md"""
#### 被试得分
"""

# ╔═╡ ebc5cfbd-dd1d-4089-8798-e35357b38cac
md"""
### 实验三
"""

# ╔═╡ 4e5c0d80-2871-4f57-a0a0-acbffb4ec199
md"""
#### 读入数据
"""

# ╔═╡ f5d03c3a-3854-4c08-9cf7-074dd628393b
md"""
#### 总体结果
"""

# ╔═╡ dec0718e-799d-48ce-b444-cf07daf803c6
md"""
#### 被试得分
"""

# ╔═╡ af57ef9e-0030-4b2d-8b1b-127f50d9754a
md"""
### 附录
"""

# ╔═╡ ef982930-6388-466d-b0e1-8d715316a884
function list2df(experiment)
	
	# 1. List csv files
	csv_list = filter(endswith(".csv"), readdir(joinpath("data", experiment), join = true))

	# 2. Read data to dataframe
	df = mapreduce(vcat, csv_list) do csv
		CSV.read(csv, DataFrame, stringtype = String,
				 drop = (i, name) -> startswith(string(name), "Column")
		)
	end

	# 3. Delete rows
	dropmissing!(df, [:mychoice, :otherchoice, :stay, :rating])
	
	# 4. Create new columns
	## a. Define a function to convert negative values to postive ones
		function neg2pos(x, y, z)
			if (x + y + z) < 0
				mn = -200
				return (Human = x-mn, Computer = y-mn, Remain = z-mn, Gain = "Lose")
			else
				return (Human = x,    Computer = y,    Remain = z,    Gain = "Gain")
			end
		end
	
	## b. Do the converting
	transform!(df,
		[:mychoice, :otherchoice, :stay] => ByRow(neg2pos)    => AsTable,
		:choicepos => ByRow(x -> ismissing(x) ? "HC" : "CH")  => :Order,
		[:mychoice, :stay] => ByRow( (x, y) -> x - y)         => :HumanRemainDiff,
		[:mychoice, :otherchoice] => ByRow( (x, y) -> x - y)  => :HumanComputerDiff
	)

	# 5. Rename column names
	rename!(df, "rating" => "Emotion", "participant"  => "Participant")
	
	# 6. Select usefule columns, "mychoice", "otherchoice", "stay"
	select!(df, 
		["Participant", "Gain", "Order", "Emotion", "Human", "Computer", "Remain",
		"HumanRemainDiff", "HumanComputerDiff"]
	)
	
	return df
end

# ╔═╡ e2d72e3d-b16e-4782-92fb-743fd332f202
ex2 = list2df("Experiment_2");

# ╔═╡ 43f57dbc-3d7e-48e0-bce1-23e058938bd1
size(ex2, 1) == 192 * 35 # 192 trial * 35 participant

# ╔═╡ 218d3696-9ee4-4841-ad53-447849a238f9
freqtable(ex2, :Gain, :Order)

# ╔═╡ ad4a9897-84fa-4e42-a653-fb36bb2e7657
ex2t = groupby(ex2, [:Participant, :Gain, :Order]);

# ╔═╡ a90937a1-1961-465a-bd7f-f8f35da9badc
ex2ts = combine(ex2t, :Emotion => mean => :Emotion);

# ╔═╡ 2eaf3273-0ac0-4cc8-8879-fd15903c75de
fmt1 = lm(@formula(Emotion ~ Gain * Order), ex2ts);

# ╔═╡ 699dd367-d4a3-431d-87a1-dd6ff276c84b
fmt2 = lm(@formula(Emotion ~ Gain), ex2ts);

# ╔═╡ a0691d8b-d80c-4b5c-b78c-163ee13faf85
ftest(fmt1.model, fmt2.model)

# ╔═╡ 71e9a4b4-5dbb-4a67-b898-0cfffd90998d
coeftable(fmt2)

# ╔═╡ cc35db1a-715b-419a-b5a7-3f4a3f8faab4
let
	replace!(ex2ts.Gain, "Gain" => "获益框架", "Lose" => "损失框架")
	transform!(ex2ts,
		:Gain => (x -> categorical(x, ordered = true)) => :Gain
	)
	xx = ex2ts.Gain
	yy = ex2ts.Emotion
	colors = Makie.default_palettes.patchcolor.val
	boxplot(Int.(xx.refs), yy, whiskerwidth = 0.5,
		color = colors[Int.(xx.refs)],
		axis = (xticks = (1:length(levels(xx)), levels(xx)),
			xlabel = "框架", ylabel = "被试情绪自评"
		)
	)
end

# ╔═╡ 1ebe900d-2301-44ea-86b3-0fd7f03769b1
HM = combine(
	groupby(ex2, [:Participant, :Gain, :Human]), 
	:Emotion => mean => :Emotion
);

# ╔═╡ a6d31357-bb6d-4fb2-a642-112f39d32832
fHM1 = lm(@formula(Emotion ~ Gain * Human), HM);

# ╔═╡ ac88d890-6514-4e82-b776-ff98467d8708
fHM2 = lm(@formula(Emotion ~ Gain + Human), HM);

# ╔═╡ 241d663c-dc41-4039-b9a2-0f48d792b8a1
ftest(fHM1.model, fHM2.model)

# ╔═╡ 2a111cc4-a80c-43d0-83b3-7c827761a4fa
coeftable(fHM2)

# ╔═╡ 5d2801db-a987-48e5-b71d-6674c0f7af58
let
	replace!(HM.Gain, "Gain" => "获益框架", "Lose" => "损失框架")
	transform!(HM,
		:Gain => (x -> categorical(x, ordered = true)) => :Gain
	)
	fig = Figure()
	ax = Axis(fig[1, 1], 
		xlabel = "被试得分", ylabel = "被试情绪自评", xticks = -200:50:200)
	for (id, gg) in enumerate(unique(HM.Gain))
		dt = HM[HM.Gain .== gg, :]
		xx = dt.Human .+ (iseven(id) ? 1 : -1) * 6.5
		yy = dt.Emotion
		boxplot!(ax, xx, yy, width = 15, label = gg, whiskerwidth = 0.5)
	end
	axislegend("框架", position = :rb)
	fig
end

# ╔═╡ af3c116c-510d-4c5c-9b64-fb56a106e5eb
ex2n = list2df("Experiment_2_N");

# ╔═╡ fe698e99-939b-44d4-aaed-1bdfdc5c58f0
ex2nts = combine(
	groupby(ex2n, [:Participant, :Gain, :Order]), 
	:Emotion => mean => :Emotion);

# ╔═╡ b4f7a35f-0f74-465b-82a0-31c404b49a10
ex2n1 = lm(@formula(Emotion ~ Gain * Order), ex2nts);

# ╔═╡ 7516d09f-7356-41ac-a565-2c4e2c21408d
ex2n2 = lm(@formula(Emotion ~ Gain + Order), ex2nts);

# ╔═╡ 3e754434-5934-42df-861f-614bfa19643f
ftest(ex2n1.model, ex2n2.model)

# ╔═╡ 00762ff1-c860-4d2c-b5ef-4a27b28b5d7f
coeftable(ex2n2)

# ╔═╡ dccfe48e-4068-4f35-8446-8476033f90e6
let
	replace!(ex2nts.Gain, "Gain" => "获益框架", "Lose" => "损失框架")
	transform!(ex2nts,
		:Gain => (x -> categorical(x, ordered = true)) => :Gain
	)
	xx = ex2nts.Gain
	yy = ex2nts.Emotion
	colors = Makie.default_palettes.patchcolor.val
	boxplot(Int.(xx.refs), yy, whiskerwidth = 0.5, color = colors[Int.(xx.refs)],
		axis = (xticks = (1:length(levels(xx)), levels(xx)), 
			xlabel = "框架", ylabel = "被试情绪自评")
	)
end

# ╔═╡ c36cb78e-09ce-4521-803f-3e94d5ebd7cd
HMn = combine(
	groupby(ex2n, [:Participant, :Gain, :Human]), 
	:Emotion => mean => :Emotion
);

# ╔═╡ fa2bd5ca-1fc5-4706-86b1-c7baf5bd2a47
fHMn1 = lm(@formula(Emotion ~ Gain * Human), HMn);

# ╔═╡ 4a90cefa-9793-481d-ae1b-863a379369ec
fHMn2 = lm(@formula(Emotion ~ Gain + Human), HMn);

# ╔═╡ dc676a84-430c-4316-9e42-e935d3dc9e74
ftest(fHMn1.model, fHMn2.model)

# ╔═╡ 366feb2b-b08e-4176-9623-149b6cbaf989
coeftable(fHMn2)

# ╔═╡ d0099447-0c7d-4573-b9b0-ff18398f36a4
let
	replace!(HMn.Gain, "Gain" => "获益框架", "Lose" => "损失框架")
	transform!(HMn,
		:Gain => (x -> categorical(x, ordered = true)) => :Gain
	)
	fig = Figure()
	ax = Axis(fig[1, 1], 
		xlabel = "被试得分", ylabel = "被试情绪自评", xticks = -200:50:200)
	for (id, gg) in enumerate(unique(HMn.Gain))
		dt = HMn[HMn.Gain .== gg, :]
		xx = dt.Human .+ (iseven(id) ? 1 : -1) * 6.5
		yy = dt.Emotion
		boxplot!(ax, xx, yy, width = 15, label = gg, whiskerwidth = 0.5)
	end
	axislegend("框架", position = :rb)
	fig
end

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PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[compat]
CSV = "~0.10.4"
CairoMakie = "~0.7.5"
CategoricalArrays = "~0.10.5"
DataFrames = "~1.3.2"
FreqTables = "~0.4.5"
GLM = "~1.6.2"
PlutoUI = "~0.7.38"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
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version = "1.2.2"

[[deps.RelocatableFolders]]
deps = ["SHA", "Scratch"]
git-tree-sha1 = "cdbd3b1338c72ce29d9584fdbe9e9b70eeb5adca"
uuid = "05181044-ff0b-4ac5-8273-598c1e38db00"
version = "0.1.3"

[[deps.Requires]]
deps = ["UUIDs"]
git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.3.0"

[[deps.Rmath]]
deps = ["Random", "Rmath_jll"]
git-tree-sha1 = "bf3188feca147ce108c76ad82c2792c57abe7b1f"
uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
version = "0.7.0"

[[deps.Rmath_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "68db32dff12bb6127bac73c209881191bf0efbb7"
uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
version = "0.3.0+0"

[[deps.SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"

[[deps.SIMD]]
git-tree-sha1 = "7dbc15af7ed5f751a82bf3ed37757adf76c32402"
uuid = "fdea26ae-647d-5447-a871-4b548cad5224"
version = "3.4.1"

[[deps.ScanByte]]
deps = ["Libdl", "SIMD"]
git-tree-sha1 = "9cc2955f2a254b18be655a4ee70bc4031b2b189e"
uuid = "7b38b023-a4d7-4c5e-8d43-3f3097f304eb"
version = "0.3.0"

[[deps.Scratch]]
deps = ["Dates"]
git-tree-sha1 = "0b4b7f1393cff97c33891da2a0bf69c6ed241fda"
uuid = "6c6a2e73-6563-6170-7368-637461726353"
version = "1.1.0"

[[deps.SentinelArrays]]
deps = ["Dates", "Random"]
git-tree-sha1 = "6a2f7d70512d205ca8c7ee31bfa9f142fe74310c"
uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
version = "1.3.12"

[[deps.Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"

[[deps.SharedArrays]]
deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"

[[deps.ShiftedArrays]]
git-tree-sha1 = "22395afdcf37d6709a5a0766cc4a5ca52cb85ea0"
uuid = "1277b4bf-5013-50f5-be3d-901d8477a67a"
version = "1.0.0"

[[deps.Showoff]]
deps = ["Dates", "Grisu"]
git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de"
uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f"
version = "1.0.3"

[[deps.SignedDistanceFields]]
deps = ["Random", "Statistics", "Test"]
git-tree-sha1 = "d263a08ec505853a5ff1c1ebde2070419e3f28e9"
uuid = "73760f76-fbc4-59ce-8f25-708e95d2df96"
version = "0.4.0"

[[deps.Sixel]]
deps = ["Dates", "FileIO", "ImageCore", "IndirectArrays", "OffsetArrays", "REPL", "libsixel_jll"]
git-tree-sha1 = "8fb59825be681d451c246a795117f317ecbcaa28"
uuid = "45858cf5-a6b0-47a3-bbea-62219f50df47"
version = "0.1.2"

[[deps.Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"

[[deps.SortingAlgorithms]]
deps = ["DataStructures"]
git-tree-sha1 = "b3363d7460f7d098ca0912c69b082f75625d7508"
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
version = "1.0.1"

[[deps.SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[[deps.SpecialFunctions]]
deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
git-tree-sha1 = "5ba658aeecaaf96923dce0da9e703bd1fe7666f9"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "2.1.4"

[[deps.StackViews]]
deps = ["OffsetArrays"]
git-tree-sha1 = "46e589465204cd0c08b4bd97385e4fa79a0c770c"
uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15"
version = "0.1.1"

[[deps.Static]]
deps = ["IfElse"]
git-tree-sha1 = "87e9954dfa33fd145694e42337bdd3d5b07021a6"
uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
version = "0.6.0"

[[deps.StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "4f6ec5d99a28e1a749559ef7dd518663c5eca3d5"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "1.4.3"

[[deps.Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[[deps.StatsAPI]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "c3d8ba7f3fa0625b062b82853a7d5229cb728b6b"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.2.1"

[[deps.StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "8977b17906b0a1cc74ab2e3a05faa16cf08a8291"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.16"

[[deps.StatsFuns]]
deps = ["ChainRulesCore", "HypergeometricFunctions", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
git-tree-sha1 = "72e6abd6fc9ef0fa62a159713c83b7637a14b2b8"
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
version = "0.9.17"

[[deps.StatsModels]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Printf", "REPL", "ShiftedArrays", "SparseArrays", "StatsBase", "StatsFuns", "Tables"]
git-tree-sha1 = "03c99c7ef267c8526953cafe3c4239656693b8ab"
uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
version = "0.6.29"

[[deps.StructArrays]]
deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"]
git-tree-sha1 = "57617b34fa34f91d536eb265df67c2d4519b8b98"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.6.5"

[[deps.SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"

[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"

[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[deps.Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"]
git-tree-sha1 = "5ce79ce186cc678bbb5c5681ca3379d1ddae11a1"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.7.0"

[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[deps.TensorCore]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6"
uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
version = "0.1.1"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.TiffImages]]
deps = ["ColorTypes", "DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "OffsetArrays", "PkgVersion", "ProgressMeter", "UUIDs"]
git-tree-sha1 = "aaa19086bc282630d82f818456bc40b4d314307d"
uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
version = "0.5.4"

[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.6"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[deps.isoband_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.3+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libsixel_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "78736dab31ae7a53540a6b752efc61f77b304c5b"
uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
version = "1.8.6+1"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
"""

# ╔═╡ Cell order:
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