Distributions
package¶The Distributions
package provides a comprehensive list of univariate, multivariate and matrix distributions and a set of generic functions that can be applied to them. See the documentation for details on the coverage.
Note that some methods refer to the distribution type (e.g. Normal
) instead of a distribution instance (e.g. Normal(0.,1.)
)
using Distributions
d = Normal() # standard normal (Gaussian) distribution
Distributions.Normal(μ=0.0, σ=1.0)
x = rand(d,20) # sample of size 20 from a standard normal
20-element Array{Float64,1}: -1.37163 0.108703 -0.614373 0.908453 -0.616237 1.30055 1.82147 1.56462 1.27457 -0.407733 -0.691858 -1.49719 0.850856 -0.250366 1.22701 -1.0103 -0.47893 0.512367 -0.433555 0.818587
de = fit_mle(Normal,x) # maximum likelihood estimates of normal dist. pars.
Distributions.Normal(μ=0.15075083842683862, σ=0.9912678731753393)
mean(de)
0.15075083842683862
std(de)
0.9912678731753393
var(de)
0.9826119963895605
kurtosis(de)
0.0
skewness(de)
0.0
loglikelihood(de,x)
-28.203361159103117
ss = suffstats(Normal,x)
Distributions.NormalStats(3.0150167685367726,0.15075083842683862,19.65223992779121,20.0)
fieldnames(ss)'
1x4 Array{Symbol,2}: :s :m :s2 :tw
fit_mle(Normal,ss) # can fit from sufficient statistics
Distributions.Normal(μ=0.15075083842683862, σ=0.9912678731753393)
It is also possible to do map
(maximum a posteriori) estimation using a conjugate prior. Again, see the documentation.
DataArrays
package¶This is a lightweight package to define types that can contain NA
's. It was split off from the DataFrames
package because loading the whole of DataFrames
takes a while. (This will change when pre-compiled packages are more easily constructed.)
The basic types defined in DataArrays
are NA
, used for literal NA input, the DataArray
, like a numeric or integer vector in R
, and the PooledDataVector
, like a factor
in R
.
The concept of an NA
is built into many of the R
types. In Julia
a DataArray
or PooledDataArray
is composed of the data and a separate bitarray
of indicators of missingness.
using DataArrays
v = @data([2,1,NA,4])
4-element DataArrays.DataArray{Int64,1}: 2 1 NA 4
fieldnames(v)
2-element Array{Symbol,1}: :data :na
typeof(v).types
svec(Array{Int64,1},BitArray{1})
v.data
4-element Array{Int64,1}: 2 1 2 4
v.na
4-element BitArray{1}: false false true false
isna(v)
4-element BitArray{1}: false false true false
dropna(v)
3-element Array{Int64,1}: 2 1 4
The PooledDataArray
, generated with the @pdata
macro call, consists of a pool
array (similar to the levels
in an R
factor
object) and a unsigned integer vector refs
.
d = Bernoulli()
sex = @pdata [rand(d) ≠ 0 ? 'F' : 'M' for i in 1:1000]
1000-element DataArrays.PooledDataArray{Char,UInt32,1}: 'M' 'F' 'M' 'M' 'M' 'F' 'F' 'F' 'M' 'F' 'M' 'F' 'F' ⋮ 'M' 'F' 'M' 'F' 'F' 'F' 'M' 'F' 'F' 'F' 'F' 'M'
sex.pool
2-element Array{Char,1}: 'F' 'M'
sex.refs
1000-element Array{UInt32,1}: 0x00000002 0x00000001 0x00000002 0x00000002 0x00000002 0x00000001 0x00000001 0x00000001 0x00000002 0x00000001 0x00000002 0x00000001 0x00000001 ⋮ 0x00000002 0x00000001 0x00000002 0x00000001 0x00000001 0x00000001 0x00000002 0x00000001 0x00000001 0x00000001 0x00000001 0x00000002
sex = compact(sex) # provides a more compact representation, if possible
1000-element DataArrays.PooledDataArray{Char,UInt8,1}: 'M' 'F' 'M' 'M' 'M' 'F' 'F' 'F' 'M' 'F' 'M' 'F' 'F' ⋮ 'M' 'F' 'M' 'F' 'F' 'F' 'M' 'F' 'F' 'F' 'F' 'M'
sex.refs
1000-element Array{UInt8,1}: 0x02 0x01 0x02 0x02 0x02 0x01 0x01 0x01 0x02 0x01 0x02 0x01 0x01 ⋮ 0x02 0x01 0x02 0x01 0x01 0x01 0x02 0x01 0x01 0x01 0x01 0x02
A few other functions common to an R
programmer, like rep
, are in the DataArrays
package.
whos(DataArrays)
/ 38 KB Function
WARNING: both StatsBase and Base export "histrange"; uses of it in module DataArrays must be qualified WARNING: both StatsBase and Base export "midpoints"; uses of it in module DataArrays must be qualified
@data 1533 bytes Function @pdata 1249 bytes Function AbstractDataArray 188 bytes DataType AbstractDataMatrix 80 bytes TypeConstructor AbstractDataVector 80 bytes TypeConstructor AbstractHistogram 228 bytes DataType CoefTable 284 bytes DataType DataArray 220 bytes DataType DataArrays 674 KB Module DataMatrix 80 bytes TypeConstructor DataVector 80 bytes TypeConstructor EachDropNA 168 bytes DataType EachFailNA 168 bytes DataType EachReplaceNA 220 bytes DataType FastPerm 284 bytes DataType Histogram 272 bytes DataType L1dist 1279 bytes Function L2dist 577 bytes Function Linfdist 1450 bytes Function NA 0 bytes DataArrays.NAtype NAException 112 bytes DataType NAtype 92 bytes DataType PooledDataArray 260 bytes DataType PooledDataMatrix 120 bytes TypeConstructor PooledDataVecs 8507 bytes Function PooledDataVector 120 bytes TypeConstructor RegressionModel 92 bytes DataType StatisticalModel 92 bytes DataType StatsBase 476 KB Module WeightVec 284 bytes DataType addcounts! 14 KB Function allna 1744 bytes Function anyna 1708 bytes Function array 5595 bytes Function autocor 1089 bytes Function autocor! 3572 bytes Function autocov 4814 bytes Function autocov! 3572 bytes Function coef 516 bytes Function coeftable 516 bytes Function compact 1438 bytes Function competerank 649 bytes Function confint 516 bytes Function corkendall 5773 bytes Function corspearman 3260 bytes Function counteq 1287 bytes Function countmap 2176 bytes Function countne 1287 bytes Function counts 8052 bytes Function crosscor 8202 bytes Function crosscor! 6952 bytes Function crosscov 8202 bytes Function crosscov! 6952 bytes Function crossentropy 2134 bytes Function cut 3992 bytes Function data 504 bytes Function denserank 647 bytes Function describe 560 bytes Function deviance 516 bytes Function df_residual 516 bytes Function dropna 2555 bytes Function each_dropna 557 bytes Function each_failNA 617 bytes Function each_failna 557 bytes Function each_replaceNA 629 bytes Function each_replacena 1015 bytes Function ecdf 948 bytes Function entropy 1799 bytes Function findat 538 bytes Function fit 13 KB Function fit! 548 bytes Function fitted 516 bytes Function geomean 1163 bytes Function getpoolidx 2027 bytes Function gkldiv 1559 bytes Function gl 2271 bytes Function harmmean 1153 bytes Function head 592 bytes Function hist 2683 bytes Function indexmap 1184 bytes Function indicatormat 4556 bytes Function inverse_rle 1942 bytes Function invsoftplus 5037 bytes Function iqr 608 bytes Function isna 4945 bytes Function kldivergence 2138 bytes Function kurtosis 5279 bytes Function levels 1717 bytes Function levelsmap 1320 bytes Function logistic 4468 bytes Function logit 4441 bytes Function loglikelihood 516 bytes Function logsumexp 2785 bytes Function mad 3645 bytes Function maxad 570 bytes Function mean_and_cov 2938 bytes Function mean_and_std 2120 bytes Function mean_and_var 2120 bytes Function meanad 585 bytes Function middle 3053 bytes Function mode 4046 bytes Function model_response 516 bytes Function modes 4963 bytes Function moment 2360 bytes Function msd 587 bytes Function nobs 516 bytes Function nquantile 590 bytes Function ordinalrank 645 bytes Function pacf 3226 bytes Function pacf! 1893 bytes Function padNA 1240 bytes Function pdata 516 bytes Function percent_change 1169 bytes Function percentile 583 bytes Function predict 516 bytes Function predict! 516 bytes Function proportionmap 1003 bytes Function proportions 7036 bytes Function psnr 655 bytes Function reldiff 1140 bytes Function removeNA 601 bytes Function reorder 1040 bytes Function rep 6024 bytes Function replace! 4684 bytes Function residuals 516 bytes Function rle 6285 bytes Function rmsd 1691 bytes Function sample 9170 bytes Function sample! 3510 bytes Function samplepair 1447 bytes Function scattermat 3208 bytes Function sem 568 bytes Function set_levels 611 bytes Function set_levels! 615 bytes Function setlevels 3164 bytes Function setlevels! 4791 bytes Function skewness 5199 bytes Function softmax 560 bytes Function softmax! 2515 bytes Function softplus 5021 bytes Function span 742 bytes Function sqL2dist 1276 bytes Function stderr 517 bytes Function summarystats 1004 bytes Function tail 607 bytes Function tiedrank 646 bytes Function trimmean 1904 bytes Function variation 1035 bytes Function vcov 516 bytes Function view 4098 bytes Function weights 1086 bytes Function wmean 723 bytes Function wmedian 1071 bytes Function wquantile 2130 bytes Function wsample 4621 bytes Function wsample! 1945 bytes Function wsum 1775 bytes Function wsum! 1904 bytes Function xlogx 4467 bytes Function xlogy 5141 bytes Function xtab 180 bytes DataType xtabs 1041 bytes Function zscore 3008 bytes Function zscore! 2904 bytes Function
DataFrames
package¶The DataFrame
type and methods for working with it are defined in the DataFrames
package. There is online documentation but, at least in my browser, the formatting is horrible. I would recommend reading the PDF file instead.
The DataFrames
package is where the formula language and types like ModelFrame
and ModelMatrix
are defined. Many familiar examples of data frames are available in the RDatasets
package.
using DataFrames, RDatasets
ds = dataset("lme4","Dyestuff")
Batch | Yield | |
---|---|---|
1 | A | 1545 |
2 | A | 1440 |
3 | A | 1440 |
4 | A | 1520 |
5 | A | 1580 |
6 | B | 1540 |
7 | B | 1555 |
8 | B | 1490 |
9 | B | 1560 |
10 | B | 1495 |
11 | C | 1595 |
12 | C | 1550 |
13 | C | 1605 |
14 | C | 1510 |
15 | C | 1560 |
16 | D | 1445 |
17 | D | 1440 |
18 | D | 1595 |
19 | D | 1465 |
20 | D | 1545 |
21 | E | 1595 |
22 | E | 1630 |
23 | E | 1515 |
24 | E | 1635 |
25 | E | 1625 |
26 | F | 1520 |
27 | F | 1455 |
28 | F | 1450 |
29 | F | 1480 |
30 | F | 1445 |
@data 1533 bytes Function @pdata 1545 bytes Function AbstractDataArray 188 bytes DataType AbstractDataMatrix 80 bytes TypeConstructor AbstractDataVector 80 bytes TypeConstructor AbstractHistogram 228 bytes DataType CoefTable 284 bytes DataType DataArray 220 bytes DataType DataArrays 803 KB Module DataMatrix 80 bytes TypeConstructor DataVector 80 bytes TypeConstructor EachDropNA 168 bytes DataType EachFailNA 168 bytes DataType EachReplaceNA 220 bytes DataType FastPerm 284 bytes DataType Histogram 272 bytes DataType L1dist 1279 bytes Function L2dist 577 bytes Function Linfdist 1450 bytes Function NA 0 bytes DataArrays.NAtype NAException 112 bytes DataType NAtype 92 bytes DataType PooledDataArray 260 bytes DataType PooledDataMatrix 120 bytes TypeConstructor PooledDataVecs 8507 bytes Function PooledDataVector 120 bytes TypeConstructor RegressionModel 92 bytes DataType StatisticalModel 92 bytes DataType StatsBase 601 KB Module WeightVec 284 bytes DataType addcounts! 14 KB Function allna 1744 bytes Function anyna 1708 bytes Function array 5595 bytes Function autocor 1089 bytes Function autocor! 3572 bytes Function autocov 4814 bytes Function autocov! 3572 bytes Function coef 516 bytes Function coeftable 516 bytes Function compact 3324 bytes Function competerank 649 bytes Function confint 516 bytes Function corkendall 5773 bytes Function corspearman 3260 bytes Function counteq 1287 bytes Function countmap 2176 bytes Function countne 1287 bytes Function counts 8052 bytes Function crosscor 8202 bytes Function crosscor! 6952 bytes Function crosscov 8202 bytes Function crosscov! 6952 bytes Function crossentropy 2134 bytes Function cut 3992 bytes Function data 504 bytes Function denserank 647 bytes Function describe 560 bytes Function deviance 516 bytes Function df_residual 516 bytes Function dropna 3439 bytes Function each_dropna 557 bytes Function each_failNA 617 bytes Function each_failna 557 bytes Function each_replaceNA 629 bytes Function each_replacena 1015 bytes Function ecdf 948 bytes Function entropy 31 KB Function findat 538 bytes Function fit 18 KB Function fit! 548 bytes Function fitted 516 bytes Function geomean 1163 bytes Function getpoolidx 2027 bytes Function gkldiv 1559 bytes Function gl 2271 bytes Function harmmean 1153 bytes Function head 592 bytes Function hist 2683 bytes Function indexmap 1184 bytes Function indicatormat 4556 bytes Function inverse_rle 1942 bytes Function invsoftplus 5037 bytes Function iqr 608 bytes Function isna 5623 bytes Function kldivergence 2638 bytes Function kurtosis 38 KB Function levels 1717 bytes Function levelsmap 1320 bytes Function logistic 4468 bytes Function logit 4441 bytes Function loglikelihood 2394 bytes Function logsumexp 2785 bytes Function mad 3645 bytes Function maxad 570 bytes Function mean_and_cov 2938 bytes Function mean_and_std 2120 bytes Function mean_and_var 2120 bytes Function meanad 585 bytes Function middle 3053 bytes Function mode 31 KB Function model_response 516 bytes Function modes 14 KB Function moment 2360 bytes Function msd 587 bytes Function nobs 516 bytes Function nquantile 590 bytes Function ordinalrank 645 bytes Function pacf 3226 bytes Function pacf! 1893 bytes Function padNA 1240 bytes Function pdata 516 bytes Function percent_change 1169 bytes Function percentile 583 bytes Function predict 516 bytes Function predict! 516 bytes Function proportionmap 1003 bytes Function proportions 7036 bytes Function psnr 655 bytes Function reldiff 1140 bytes Function removeNA 601 bytes Function reorder 1040 bytes Function rep 6024 bytes Function replace! 4684 bytes Function residuals 516 bytes Function rle 6285 bytes Function rmsd 1691 bytes Function sample 9170 bytes Function sample! 3510 bytes Function samplepair 1447 bytes Function scattermat 3208 bytes Function sem 568 bytes Function set_levels 611 bytes Function set_levels! 615 bytes Function setlevels 3164 bytes Function setlevels! 4791 bytes Function skewness 36 KB Function softmax 560 bytes Function softmax! 2515 bytes Function softplus 5021 bytes Function span 742 bytes Function sqL2dist 1276 bytes Function stderr 517 bytes Function summarystats 1004 bytes Function tail 607 bytes Function tiedrank 646 bytes Function trimmean 1904 bytes Function variation 1035 bytes Function vcov 516 bytes Function view 4098 bytes Function weights 1086 bytes Function wmean 723 bytes Function wmedian 1071 bytes Function wquantile 2130 bytes Function wsample 4621 bytes Function wsample! 1945 bytes Function wsum 1775 bytes Function wsum! 1904 bytes Function xlogx 4467 bytes Function xlogy 5141 bytes Function xtab 180 bytes DataType xtabs 1041 bytes Function zscore 3008 bytes Function zscore! 2904 bytes Function
WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/bates/.julia/v0.4/RDatasets/src/dataset.jl:1 WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/bates/.julia/v0.4/RDatasets/src/dataset.jl:1 WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/bates/.julia/v0.4/RDatasets/src/datasets.jl:1
names(ds)
2-element Array{Symbol,1}: :Batch :Yield
Indivual columns can be accessed by name using symbols (e.g. :Yield
). This means that the column names should be valid Julia identifiers. Among other things, they cannot contain the dot or period character (.
).
ds[:Yield]
30-element DataArrays.DataArray{Int32,1}: 1545 1440 1440 1520 1580 1540 1555 1490 1560 1495 1595 1550 1605 ⋮ 1465 1545 1595 1630 1515 1635 1625 1520 1455 1450 1480 1445
The DataFrame
constructor can be given <name>=<value>
pairs.
x = 1.:10.;
ϵ = rand(Normal(0.,0.1),length(x));
β = [4.2,1.1];
ytrue = [ones(length(x)) x]*β;
dd = DataFrame(x=x,ytrue = ytrue, y = ytrue + ϵ)
x | ytrue | y | |
---|---|---|---|
1 | 1.0 | 5.300000000000001 | 5.4266459954573465 |
2 | 2.0 | 6.4 | 6.547618585779938 |
3 | 3.0 | 7.5 | 7.4126925420952325 |
4 | 4.0 | 8.600000000000001 | 8.502478506094308 |
5 | 5.0 | 9.7 | 9.459142933970893 |
6 | 6.0 | 10.8 | 10.798058744556116 |
7 | 7.0 | 11.900000000000002 | 11.791803744236113 |
8 | 8.0 | 13.0 | 13.131877566162375 |
9 | 9.0 | 14.100000000000001 | 14.145992654678988 |
10 | 10.0 | 15.2 | 15.120811027815394 |
In R
many modeling functions that use a formula/data representation first apply model.frame
then model.matrix
. In the DataFrames
package these are ModelFrame
and ModelMatrix
. A ModelFrame
is the reduction of the original DataFrame
to only those columns that are used in the model and after application of the NA action. It includes a Terms
object, which describes the terms in the formula, again after some reduction and expansion. Finally, a record is kept of which rows in the original data frame are represented in the derived frame.
mf = ModelFrame(y ~ x, dd)
DataFrames.ModelFrame(10x2 DataFrames.DataFrame | Row | y | x | |-----|---------|------| | 1 | 5.42665 | 1.0 | | 2 | 6.54762 | 2.0 | | 3 | 7.41269 | 3.0 | | 4 | 8.50248 | 4.0 | | 5 | 9.45914 | 5.0 | | 6 | 10.7981 | 6.0 | | 7 | 11.7918 | 7.0 | | 8 | 13.1319 | 8.0 | | 9 | 14.146 | 9.0 | | 10 | 15.1208 | 10.0 |,DataFrames.Terms(Any[:x],Any[:y,:x],2x2 Array{Int8,2}: 1 0 0 1,[1,1],true,true),Bool[true,true,true,true,true,true,true,true,true,true])
The ModelMatrix
is constructed from the ModelFrame
.
mm = ModelMatrix(mf)
DataFrames.ModelMatrix{Float64}(10x2 Array{Float64,2}: 1.0 1.0 1.0 2.0 1.0 3.0 1.0 4.0 1.0 5.0 1.0 6.0 1.0 7.0 1.0 8.0 1.0 9.0 1.0 10.0,[0,1])
The assign
vector in this object maps columns to terms. It is used when performing hypothesis tests, like anova
. At present the model_response
function returns the value of the expression on the left-hand side of the formula.
model_response(mf)
10-element Array{Float64,1}: 5.42665 6.54762 7.41269 8.50248 9.45914 10.7981 11.7918 13.1319 14.146 15.1208
These facilities are not developed as fully as those in R
.
The GLM
package provides functions to fit and analyse the linear models and generalized linear models.
using GLM
fm = lm(y ~ x, dd)
DataFrames.DataFrameRegressionModel{GLM.LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredQR{Float64}},Float64}: Coefficients: Estimate Std.Error t value Pr(>|t|) (Intercept) 4.22575 0.0913301 46.269 <1e-10 x 1.09236 0.0147192 74.2132 <1e-11
fm = fit(LinearModel,y ~ x,dd)
DataFrames.DataFrameRegressionModel{GLM.LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredQR{Float64}},Float64}: Coefficients: Estimate Std.Error t value Pr(>|t|) (Intercept) 4.22575 0.0913301 46.269 <1e-10 x 1.09236 0.0147192 74.2132 <1e-11
The StatsBase
package contains functions for sample statistics and many utilities. There is online documentation. Much of the design and implementation is by Dahua Lin who is a stickler for extracting every last ounce of performance.
using StatsBase
whos(StatsBase)
AbstractHistogram 228 bytes DataType CoefTable 284 bytes DataType Histogram 272 bytes DataType L1dist 1279 bytes Function L2dist 577 bytes Function Linfdist 1450 bytes Function RegressionModel 92 bytes DataType StatisticalModel 92 bytes DataType StatsBase 470 KB Module WeightVec 284 bytes DataType addcounts! 11 KB Function autocor 4814 bytes Function autocor! 3572 bytes Function autocov 4814 bytes Function autocov! 3572 bytes Function coef 516 bytes Function coeftable 516 bytes Function competerank 649 bytes Function confint 516 bytes Function corkendall 5773 bytes Function corspearman 2532 bytes Function counteq 1287 bytes Function countmap 1118 bytes Function countne 1287 bytes Function counts 8052 bytes Function crosscor 8202 bytes Function crosscor! 6952 bytes Function crosscov 8202 bytes Function crosscov! 6952 bytes Function crossentropy 2134 bytes Function denserank 647 bytes Function describe 560 bytes Function deviance 516 bytes Function df_residual 516 bytes Function ecdf 948 bytes Function entropy 1799 bytes Function findat 538 bytes Function fit 13 KB Function fit! 548 bytes Function fitted 516 bytes Function geomean 1163 bytes Function gkldiv 1559 bytes Function harmmean 1153 bytes Function hist 2683 bytes Function histrange 5943 bytes Function indexmap 1184 bytes Function indicatormat 4556 bytes Function inverse_rle 1798 bytes Function invsoftplus 5037 bytes Function iqr 608 bytes Function kldivergence 2138 bytes Function kurtosis 4745 bytes Function levelsmap 1320 bytes Function logistic 4468 bytes Function logit 4441 bytes Function loglikelihood 516 bytes Function logsumexp 2785 bytes Function mad 3116 bytes Function maxad 570 bytes Function mean_and_cov 2938 bytes Function mean_and_std 2120 bytes Function mean_and_var 2120 bytes Function meanad 585 bytes Function middle 3053 bytes Function midpoints 1657 bytes Function mode 4046 bytes Function model_response 516 bytes Function modes 4963 bytes Function moment 2360 bytes Function msd 587 bytes Function nobs 516 bytes Function nquantile 590 bytes Function ordinalrank 645 bytes Function pacf 3226 bytes Function pacf! 1893 bytes Function percentile 583 bytes Function predict 516 bytes Function predict! 516 bytes Function proportionmap 1003 bytes Function proportions 7036 bytes Function psnr 655 bytes Function residuals 516 bytes Function rle 1824 bytes Function rmsd 1691 bytes Function sample 9170 bytes Function sample! 3510 bytes Function samplepair 1447 bytes Function scattermat 3208 bytes Function sem 568 bytes Function skewness 4665 bytes Function softmax 560 bytes Function softmax! 2515 bytes Function softplus 5021 bytes Function span 742 bytes Function sqL2dist 1276 bytes Function stderr 517 bytes Function summarystats 1004 bytes Function tiedrank 646 bytes Function trimmean 1904 bytes Function variation 1035 bytes Function vcov 516 bytes Function view 4098 bytes Function weights 1086 bytes Function wmean 723 bytes Function wmedian 1071 bytes Function wquantile 2130 bytes Function wsample 4621 bytes Function wsample! 1945 bytes Function wsum 1775 bytes Function wsum! 1904 bytes Function xlogx 4467 bytes Function xlogy 5141 bytes Function zscore 3008 bytes Function zscore! 2904 bytes Function
The MLBase
package contains many functions for data manipulation and reduction. It uses the Machine Learning (ML) terminology.
using MLBase
whos(MLBase)
WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/juser/.julia/v0.4/MLBase/src/modeltune.jl:5 WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/juser/.julia/v0.4/MLBase/src/modeltune.jl:5 WARNING: Base.String is deprecated, use AbstractString instead. likely near /home/juser/.julia/v0.4/MLBase/src/modeltune.jl:5 WARNING: Base.FloatingPoint is deprecated, use AbstractFloat instead. likely near /home/juser/.julia/v0.4/MLBase/src/deprecated/datapre.jl:104 WARNING: Base.FloatingPoint is deprecated, use AbstractFloat instead. likely near /home/juser/.julia/v0.4/MLBase/src/deprecated/datapre.jl:105 WARNING: Base.FloatingPoint is deprecated, use AbstractFloat instead. likely near /home/juser/.julia/v0.4/MLBase/src/deprecated/datapre.jl:163 WARNING: Base.FloatingPoint is deprecated, use AbstractFloat instead. likely near /home/juser/.julia/v0.4/MLBase/src/deprecated/datapre.jl:163 WARNING: Base.FloatingPoint is deprecated, use AbstractFloat instead. likely near /home/juser/.julia/v0.4/MLBase/src/deprecated/datapre.jl:163
AbstractHistogram 228 bytes DataType
WARNING: both StatsBase and Base export "histrange"; uses of it in module MLBase must be qualified WARNING: both StatsBase and Base export "midpoints"; uses of it in module MLBase must be qualified
CoefTable 284 bytes DataType CrossValGenerator 92 bytes DataType Forward 0 bytes Base.Order.ForwardOrdering Histogram 272 bytes DataType Kfold 136 bytes DataType L1dist 1279 bytes Function L2dist 577 bytes Function LOOCV 112 bytes DataType LabelMap 180 bytes DataType Linfdist 1450 bytes Function MLBase 369 KB Module ROCNums 228 bytes DataType RandomSub 136 bytes DataType RegressionModel 92 bytes DataType Reverse 0 bytes Base.Order.ReverseOrdering{Base.Or… Standardize 136 bytes DataType StatisticalModel 92 bytes DataType StatsBase 470 KB Module StratifiedKfold 148 bytes DataType StratifiedRandomSub 148 bytes DataType WeightVec 284 bytes DataType addcounts! 11 KB Function autocor 4814 bytes Function autocor! 3572 bytes Function autocov 4814 bytes Function autocov! 3572 bytes Function classify 4926 bytes Function classify! 3564 bytes Function classify_withscore 1934 bytes Function classify_withscores 1325 bytes Function classify_withscores! 2395 bytes Function coef 516 bytes Function coeftable 516 bytes Function competerank 649 bytes Function confint 516 bytes Function confusmat 1518 bytes Function corkendall 5773 bytes Function correctrate 611 bytes Function corspearman 2532 bytes Function counteq 1287 bytes Function counthits 4733 bytes Function countmap 1118 bytes Function countne 1287 bytes Function counts 8052 bytes Function cross_validate 2492 bytes Function crosscor 8202 bytes Function crosscor! 6952 bytes Function crosscov 8202 bytes Function crosscov! 6952 bytes Function crossentropy 2134 bytes Function denserank 647 bytes Function describe 560 bytes Function deviance 516 bytes Function df_residual 516 bytes Function ecdf 948 bytes Function entropy 1799 bytes Function errorrate 611 bytes Function f1score 604 bytes Function false_negative 496 bytes Function false_negative_rate 521 bytes Function false_positive 496 bytes Function false_positive_rate 521 bytes Function findat 538 bytes Function fit 13 KB Function fit! 548 bytes Function fitted 516 bytes Function geomean 1163 bytes Function gkldiv 1559 bytes Function gridtune 2063 bytes Function groupindices 3382 bytes Function harmmean 1153 bytes Function hist 2683 bytes Function hitrate 731 bytes Function hitrates 1422 bytes Function indexmap 1184 bytes Function indicatormat 4556 bytes Function inverse_rle 1798 bytes Function invsoftplus 5037 bytes Function iqr 608 bytes Function kldivergence 2138 bytes Function kurtosis 4745 bytes Function labeldecode 1648 bytes Function labelencode 1657 bytes Function labelmap 1417 bytes Function levelsmap 1320 bytes Function logistic 4468 bytes Function logit 4441 bytes Function loglikelihood 516 bytes Function logsumexp 2785 bytes Function mad 3116 bytes Function maxad 570 bytes Function mean_and_cov 2938 bytes Function mean_and_std 2120 bytes Function mean_and_var 2120 bytes Function meanad 585 bytes Function middle 3053 bytes Function mode 4046 bytes Function model_response 516 bytes Function modes 4963 bytes Function moment 2360 bytes Function msd 587 bytes Function nobs 516 bytes Function nquantile 590 bytes Function ordinalrank 645 bytes Function pacf 3226 bytes Function pacf! 1893 bytes Function percentile 583 bytes Function precision 3287 bytes Function predict 516 bytes Function predict! 516 bytes Function proportionmap 1003 bytes Function proportions 7036 bytes Function psnr 655 bytes Function recall 506 bytes Function repeach 3277 bytes Function repeachcol 3354 bytes Function repeachrow 4030 bytes Function residuals 516 bytes Function rle 1824 bytes Function rmsd 1691 bytes Function roc 15 KB Function sample 9170 bytes Function sample! 3510 bytes Function samplepair 1447 bytes Function scattermat 3208 bytes Function sem 568 bytes Function skewness 4665 bytes Function softmax 560 bytes Function softmax! 2515 bytes Function softplus 5021 bytes Function span 742 bytes Function sqL2dist 1276 bytes Function standardize 1819 bytes Function standardize! 1821 bytes Function stderr 517 bytes Function summarystats 1004 bytes Function tiedrank 646 bytes Function transform 1086 bytes Function trimmean 1904 bytes Function true_negative 496 bytes Function true_negative_rate 521 bytes Function true_positive 496 bytes Function true_positive_rate 521 bytes Function variation 1035 bytes Function vcov 516 bytes Function view 4098 bytes Function weights 1086 bytes Function wmean 723 bytes Function wmedian 1071 bytes Function wquantile 2130 bytes Function wsample 4621 bytes Function wsample! 1945 bytes Function wsum 1775 bytes Function wsum! 1904 bytes Function xlogx 4467 bytes Function xlogy 5141 bytes Function zscore 3008 bytes Function zscore! 2904 bytes Function
using RCall
form = rcopy("Formaldehyde")
carb | optden | |
---|---|---|
1 | 0.1 | 0.086 |
2 | 0.3 | 0.269 |
3 | 0.5 | 0.446 |
4 | 0.6 | 0.538 |
5 | 0.7 | 0.626 |
6 | 0.9 | 0.782 |
@rimport lme4
WARNING: RCall.jl Loading required package: Matrix
whos(lme4)
Arabidopsis 8 bytes RCall.RObject{RCall.VecSxp} Cv_to_Sv 8 bytes RCall.RObject{RCall.ClosSxp} Cv_to_Vv 8 bytes RCall.RObject{RCall.ClosSxp} Dyestuff 8 bytes RCall.RObject{RCall.VecSxp} Dyestuff2 8 bytes RCall.RObject{RCall.VecSxp} GHrule 8 bytes RCall.RObject{RCall.ClosSxp} GQN 8 bytes RCall.RObject{RCall.VecSxp} GQdk 8 bytes RCall.RObject{RCall.ClosSxp} InstEval 8 bytes RCall.RObject{RCall.VecSxp} NelderMead 8 bytes RCall.RObject{RCall.ClosSxp} Nelder_Mead 8 bytes RCall.RObject{RCall.ClosSxp} Pastes 8 bytes RCall.RObject{RCall.VecSxp} Penicillin 8 bytes RCall.RObject{RCall.VecSxp} REMLcrit 8 bytes RCall.RObject{RCall.ClosSxp} Sv_to_Cv 8 bytes RCall.RObject{RCall.ClosSxp} VarCorr 8 bytes RCall.RObject{RCall.ClosSxp} VerbAgg 8 bytes RCall.RObject{RCall.VecSxp} Vv_to_Cv 8 bytes RCall.RObject{RCall.ClosSxp} bootMer 8 bytes RCall.RObject{RCall.ClosSxp} cake 8 bytes RCall.RObject{RCall.VecSxp} cbpp 8 bytes RCall.RObject{RCall.VecSxp} confint.merMod 8 bytes RCall.RObject{RCall.ClosSxp} cov2sdcor 8 bytes RCall.RObject{RCall.ClosSxp} devcomp 8 bytes RCall.RObject{RCall.ClosSxp} dummy 8 bytes RCall.RObject{RCall.ClosSxp} expandDoubleVerts 8 bytes RCall.RObject{RCall.ClosSxp} factorize 8 bytes RCall.RObject{RCall.ClosSxp} findbars 8 bytes RCall.RObject{RCall.ClosSxp} fixef 8 bytes RCall.RObject{RCall.ClosSxp} formatVC 8 bytes RCall.RObject{RCall.ClosSxp} fortify.merMod 8 bytes RCall.RObject{RCall.ClosSxp} getL 8 bytes RCall.RObject{RCall.ClosSxp} getME 8 bytes RCall.RObject{RCall.ClosSxp} glFormula 8 bytes RCall.RObject{RCall.ClosSxp} glmFamily 8 bytes RCall.RObject{RCall.ClosSxp} glmResp 8 bytes RCall.RObject{RCall.ClosSxp} glmer 8 bytes RCall.RObject{RCall.ClosSxp} glmer.nb 8 bytes RCall.RObject{RCall.ClosSxp} glmerControl 8 bytes RCall.RObject{RCall.ClosSxp} glmerLaplaceHandle 8 bytes RCall.RObject{RCall.ClosSxp} golden 8 bytes RCall.RObject{RCall.ClosSxp} grouseticks 8 bytes RCall.RObject{RCall.VecSxp} grouseticks_agg 8 bytes RCall.RObject{RCall.VecSxp} isGLMM 8 bytes RCall.RObject{RCall.ClosSxp} isLMM 8 bytes RCall.RObject{RCall.ClosSxp} isNLMM 8 bytes RCall.RObject{RCall.ClosSxp} isNested 8 bytes RCall.RObject{RCall.ClosSxp} isREML 8 bytes RCall.RObject{RCall.ClosSxp} lFormula 8 bytes RCall.RObject{RCall.ClosSxp} llikAIC 8 bytes RCall.RObject{RCall.ClosSxp} lmList 8 bytes RCall.RObject{RCall.ClosSxp} lmResp 8 bytes RCall.RObject{RCall.ClosSxp} lme4 756 bytes Module lmer 8 bytes RCall.RObject{RCall.ClosSxp} lmerControl 8 bytes RCall.RObject{RCall.ClosSxp} lmerResp 8 bytes RCall.RObject{RCall.ClosSxp} logProf 8 bytes RCall.RObject{RCall.ClosSxp} merPredD 8 bytes RCall.RObject{RCall.ClosSxp} methTitle 8 bytes RCall.RObject{RCall.ClosSxp} mkDataTemplate 8 bytes RCall.RObject{RCall.ClosSxp} mkGlmerDevfun 8 bytes RCall.RObject{RCall.ClosSxp} mkLmerDevfun 8 bytes RCall.RObject{RCall.ClosSxp} mkMerMod 8 bytes RCall.RObject{RCall.ClosSxp} mkParsTemplate 8 bytes RCall.RObject{RCall.ClosSxp} mkReTrms 8 bytes RCall.RObject{RCall.ClosSxp} mkRespMod 8 bytes RCall.RObject{RCall.ClosSxp} mkVarCorr 8 bytes RCall.RObject{RCall.ClosSxp} mlist2vec 8 bytes RCall.RObject{RCall.ClosSxp} negative.binomial 8 bytes RCall.RObject{RCall.ClosSxp} ngrps 8 bytes RCall.RObject{RCall.ClosSxp} nlformula 8 bytes RCall.RObject{RCall.ClosSxp} nlmer 8 bytes RCall.RObject{RCall.ClosSxp} nlmerControl 8 bytes RCall.RObject{RCall.ClosSxp} nloptwrap 8 bytes RCall.RObject{RCall.ClosSxp} nlsResp 8 bytes RCall.RObject{RCall.ClosSxp} nobars 8 bytes RCall.RObject{RCall.ClosSxp} optimizeGlmer 8 bytes RCall.RObject{RCall.ClosSxp} optimizeLmer 8 bytes RCall.RObject{RCall.ClosSxp} ranef 8 bytes RCall.RObject{RCall.ClosSxp} rePos 8 bytes RCall.RObject{RCall.ClosSxp} refit 8 bytes RCall.RObject{RCall.ClosSxp} refitML 8 bytes RCall.RObject{RCall.ClosSxp} sdcor2cov 8 bytes RCall.RObject{RCall.ClosSxp} show 8 bytes RCall.RObject{RCall.ClosSxp} sigma 8 bytes RCall.RObject{RCall.ClosSxp} sleepstudy 8 bytes RCall.RObject{RCall.VecSxp} subbars 8 bytes RCall.RObject{RCall.ClosSxp} updateGlmerDevfun 8 bytes RCall.RObject{RCall.ClosSxp} varianceProf 8 bytes RCall.RObject{RCall.ClosSxp} vcov.merMod 8 bytes RCall.RObject{RCall.ClosSxp} vec2STlist 8 bytes RCall.RObject{RCall.ClosSxp} vec2mlist 8 bytes RCall.RObject{RCall.ClosSxp}
lme4.grouseticks
RCall.RObject{RCall.VecSxp} INDEX TICKS BROOD HEIGHT YEAR LOCATION cHEIGHT 1 1 0 501 465 95 32 2.759305 2 2 0 501 465 95 32 2.759305 3 3 0 502 472 95 36 9.759305 4 4 0 503 475 95 37 12.759305 5 5 0 503 475 95 37 12.759305 6 6 3 503 475 95 37 12.759305 7 7 2 503 475 95 37 12.759305 8 8 0 504 488 95 44 25.759305 9 9 0 504 488 95 44 25.759305 10 10 2 504 488 95 44 25.759305 11 11 0 505 492 95 47 29.759305 12 12 0 505 492 95 47 29.759305 13 13 0 505 492 95 47 29.759305 14 14 0 506 490 95 45 27.759305 15 15 0 506 490 95 45 27.759305 16 16 0 506 490 95 45 27.759305 17 17 0 507 464 95 31 1.759305 18 18 0 507 464 95 31 1.759305 19 19 0 507 464 95 31 1.759305 20 20 1 507 464 95 31 1.759305 21 21 2 507 464 95 31 1.759305 22 22 1 509 457 95 28 -5.240695 23 23 0 510 457 95 28 -5.240695 24 24 0 511 457 95 28 -5.240695 25 25 5 511 457 95 28 -5.240695 26 26 8 512 451 95 26 -11.240695 27 27 3 512 451 95 26 -11.240695 28 28 4 512 451 95 26 -11.240695 29 29 7 513 437 95 17 -25.240695 30 30 0 513 437 95 17 -25.240695 31 31 4 513 437 95 17 -25.240695 32 32 4 514 430 95 14 -32.240695 33 33 1 514 430 95 14 -32.240695 34 34 0 514 430 95 14 -32.240695 35 35 0 514 430 95 14 -32.240695 36 36 3 514 430 95 14 -32.240695 37 37 1 514 430 95 14 -32.240695 38 38 6 515 427 95 13 -35.240695 39 39 0 515 427 95 13 -35.240695 40 40 1 515 427 95 13 -35.240695 41 41 0 515 427 95 13 -35.240695 42 42 2 516 419 95 7 -43.240695 43 43 7 516 419 95 7 -43.240695 44 44 31 516 419 95 7 -43.240695 45 45 34 517 411 95 4 -51.240695 46 46 17 517 411 95 4 -51.240695 47 47 16 517 411 95 4 -51.240695 48 48 66 518 419 95 7 -43.240695 49 49 49 518 419 95 7 -43.240695 50 50 82 518 419 95 7 -43.240695 51 51 85 518 419 95 7 -43.240695 52 52 64 518 419 95 7 -43.240695 53 53 11 519 424 95 11 -38.240695 54 54 14 519 424 95 11 -38.240695 55 55 4 519 424 95 11 -38.240695 56 56 10 519 424 95 11 -38.240695 57 57 3 520 427 95 13 -35.240695 58 58 15 520 427 95 13 -35.240695 59 59 8 520 427 95 13 -35.240695 60 60 9 521 422 95 9 -40.240695 61 61 11 521 422 95 9 -40.240695 62 62 7 521 422 95 9 -40.240695 63 63 13 521 422 95 9 -40.240695 64 64 3 522 503 95 53 40.759305 65 65 0 523 496 95 51 33.759305 66 66 0 523 496 95 51 33.759305 67 67 1 523 496 95 51 33.759305 68 68 1 523 496 95 51 33.759305 69 69 0 525 507 95 54 44.759305 70 70 0 525 507 95 54 44.759305 71 71 0 525 507 95 54 44.759305 72 72 0 525 507 95 54 44.759305 73 73 0 526 496 95 51 33.759305 74 74 0 526 496 95 51 33.759305 75 75 0 526 496 95 51 33.759305 76 76 1 526 496 95 51 33.759305 77 77 1 526 496 95 51 33.759305 78 78 0 526 496 95 51 33.759305 79 79 2 528 466 95 33 3.759305 80 80 0 528 466 95 33 3.759305 81 81 3 528 466 95 33 3.759305 82 82 1 531 488 95 44 25.759305 83 83 7 533 442 95 19 -20.240695 84 84 2 533 442 95 19 -20.240695 85 85 16 533 442 95 19 -20.240695 86 86 12 533 442 95 19 -20.240695 87 87 0 533 442 95 19 -20.240695 88 88 1 535 442 95 19 -20.240695 89 89 0 537 533 95 63 70.759305 90 90 0 537 533 95 63 70.759305 91 91 1 537 533 95 63 70.759305 92 92 0 537 533 95 63 70.759305 93 93 0 537 533 95 63 70.759305 94 94 1 537 533 95 63 70.759305 95 95 0 537 533 95 63 70.759305 96 96 0 538 533 95 63 70.759305 97 97 0 539 515 95 59 52.759305 98 98 0 539 515 95 59 52.759305 99 99 0 539 515 95 59 52.759305 100 100 0 539 515 95 59 52.759305 101 101 5 540 518 95 60 55.759305 102 102 2 540 518 95 60 55.759305 103 103 2 542 493 95 48 30.759305 104 104 1 542 493 95 48 30.759305 105 105 1 542 493 95 48 30.759305 106 106 0 548 468 95 34 5.759305 107 107 0 548 468 95 34 5.759305 108 108 1 548 468 95 34 5.759305 109 109 1 549 476 95 38 13.759305 110 110 1 549 476 95 38 13.759305 111 111 0 549 476 95 38 13.759305 112 112 0 549 476 95 38 13.759305 113 113 5 550 446 95 22 -16.240695 114 114 3 550 446 95 22 -16.240695 115 115 2 553 460 95 30 -2.240695 116 116 2 559 525 95 62 62.759305 117 117 1 559 525 95 62 62.759305 118 118 1 601 410 96 3 -52.240695 119 119 0 601 410 96 3 -52.240695 120 120 2 601 410 96 3 -52.240695 121 121 5 601 410 96 3 -52.240695 122 122 1 601 410 96 3 -52.240695 123 123 2 601 410 96 3 -52.240695 124 124 2 601 410 96 3 -52.240695 125 125 3 602 417 96 6 -45.240695 126 126 14 602 417 96 6 -45.240695 127 127 11 602 417 96 6 -45.240695 128 128 9 602 417 96 6 -45.240695 129 129 4 602 417 96 6 -45.240695 130 130 10 602 417 96 6 -45.240695 131 131 33 602 417 96 6 -45.240695 132 132 19 602 417 96 6 -45.240695 133 133 16 602 417 96 6 -45.240695 134 134 16 603 430 96 14 -32.240695 135 135 13 603 430 96 14 -32.240695 136 136 11 603 430 96 14 -32.240695 137 137 7 603 430 96 14 -32.240695 138 138 4 603 430 96 14 -32.240695 139 139 11 603 430 96 14 -32.240695 140 140 1 604 456 96 27 -6.240695 141 141 1 604 456 96 27 -6.240695 142 142 4 604 456 96 27 -6.240695 143 143 6 605 457 96 28 -5.240695 144 144 2 605 457 96 28 -5.240695 145 145 7 605 457 96 28 -5.240695 146 146 8 605 457 96 28 -5.240695 147 147 14 605 457 96 28 -5.240695 148 148 6 606 430 96 14 -32.240695 149 149 13 606 430 96 14 -32.240695 150 150 5 606 430 96 14 -32.240695 151 151 8 606 430 96 14 -32.240695 152 152 13 606 430 96 14 -32.240695 153 153 17 606 430 96 14 -32.240695 154 154 5 606 430 96 14 -32.240695 155 155 1 606 430 96 14 -32.240695 156 156 1 606 430 96 14 -32.240695 157 157 2 606 430 96 14 -32.240695 158 158 7 607 423 96 10 -39.240695 159 159 7 608 421 96 8 -41.240695 160 160 11 608 421 96 8 -41.240695 161 161 1 609 525 96 62 62.759305 162 162 0 609 525 96 62 62.759305 163 163 5 610 509 96 55 46.759305 164 164 4 610 509 96 55 46.759305 165 165 0 611 499 96 52 36.759305 166 166 0 611 499 96 52 36.759305 167 167 0 611 499 96 52 36.759305 168 168 7 612 503 96 53 40.759305 169 169 5 612 503 96 53 40.759305 170 170 3 612 503 96 53 40.759305 171 171 1 612 503 96 53 40.759305 172 172 6 612 503 96 53 40.759305 173 173 1 614 492 96 47 29.759305 174 174 2 614 492 96 47 29.759305 175 175 14 615 491 96 46 28.759305 176 176 5 615 491 96 46 28.759305 177 177 27 615 491 96 46 28.759305 178 178 1 616 475 96 37 12.759305 179 179 2 616 475 96 37 12.759305 180 180 3 616 475 96 37 12.759305 181 181 0 616 475 96 37 12.759305 182 182 1 617 479 96 40 16.759305 183 183 0 617 479 96 40 16.759305 184 184 5 617 479 96 40 16.759305 185 185 5 617 479 96 40 16.759305 186 186 8 617 479 96 40 16.759305 187 187 21 617 479 96 40 16.759305 188 188 15 618 472 96 36 9.759305 189 189 15 618 472 96 36 9.759305 190 190 6 618 472 96 36 9.759305 191 191 19 618 472 96 36 9.759305 192 192 14 618 472 96 36 9.759305 193 193 1 621 485 96 42 22.759305 194 194 1 621 485 96 42 22.759305 195 195 3 621 485 96 42 22.759305 196 196 2 621 485 96 42 22.759305 197 197 3 621 485 96 42 22.759305 198 198 2 623 495 96 50 32.759305 199 199 5 623 495 96 50 32.759305 200 200 0 624 472 96 36 9.759305 201 201 6 624 472 96 36 9.759305 202 202 3 624 472 96 36 9.759305 203 203 1 625 458 96 29 -4.240695 204 204 0 625 458 96 29 -4.240695 205 205 1 625 458 96 29 -4.240695 206 206 6 625 458 96 29 -4.240695 207 207 1 625 458 96 29 -4.240695 208 208 85 626 449 96 24 -13.240695 209 209 45 626 449 96 24 -13.240695 210 210 68 626 449 96 24 -13.240695 211 211 84 626 449 96 24 -13.240695 212 212 50 626 449 96 24 -13.240695 213 213 13 628 442 96 19 -20.240695 214 214 1 628 442 96 19 -20.240695 215 215 19 629 448 96 23 -14.240695 216 216 26 629 448 96 23 -14.240695 217 217 9 629 448 96 23 -14.240695 218 218 2 629 448 96 23 -14.240695 219 219 4 629 448 96 23 -14.240695 220 220 3 629 448 96 23 -14.240695 221 221 22 630 448 96 23 -14.240695 222 222 32 630 448 96 23 -14.240695 223 223 5 631 403 96 1 -59.240695 224 224 21 631 403 96 1 -59.240695 225 225 26 631 403 96 1 -59.240695 226 226 13 631 403 96 1 -59.240695 227 227 23 631 403 96 1 -59.240695 228 228 42 632 411 96 4 -51.240695 229 229 38 632 411 96 4 -51.240695 230 230 61 632 411 96 4 -51.240695 231 231 79 632 411 96 4 -51.240695 232 232 39 632 411 96 4 -51.240695 233 233 41 632 411 96 4 -51.240695 234 234 15 634 415 96 5 -47.240695 235 235 23 634 415 96 5 -47.240695 236 236 14 634 415 96 5 -47.240695 237 237 7 635 427 96 13 -35.240695 238 238 24 636 424 96 11 -38.240695 239 239 3 638 525 96 62 62.759305 240 240 1 638 525 96 62 62.759305 241 241 2 640 521 96 61 58.759305 242 242 1 640 521 96 61 58.759305 243 243 0 640 521 96 61 58.759305 244 244 3 641 518 96 60 55.759305 245 245 8 641 518 96 60 55.759305 246 246 1 642 495 96 50 32.759305 247 247 2 642 495 96 50 32.759305 248 248 0 642 495 96 50 32.759305 249 249 8 643 495 96 50 32.759305 250 250 3 643 495 96 50 32.759305 251 251 14 643 495 96 50 32.759305 252 252 16 643 495 96 50 32.759305 253 253 18 643 495 96 50 32.759305 254 254 11 643 495 96 50 32.759305 255 255 13 643 495 96 50 32.759305 256 256 6 645 460 96 30 -2.240695 257 257 7 645 460 96 30 -2.240695 258 258 10 645 460 96 30 -2.240695 259 259 5 647 442 96 19 -20.240695 260 260 7 647 442 96 19 -20.240695 261 261 25 648 443 96 20 -19.240695 262 262 11 648 443 96 20 -19.240695 263 263 6 648 443 96 20 -19.240695 264 264 4 648 443 96 20 -19.240695 265 265 7 648 443 96 20 -19.240695 266 266 4 650 425 96 12 -37.240695 267 267 6 650 425 96 12 -37.240695 268 268 2 650 425 96 12 -37.240695 269 269 5 651 439 96 18 -23.240695 270 270 3 651 439 96 18 -23.240695 271 271 7 651 439 96 18 -23.240695 272 272 3 652 444 96 21 -18.240695 273 273 1 701 450 97 25 -12.240695 274 274 4 701 450 97 25 -12.240695 275 275 4 701 450 97 25 -12.240695 276 276 2 701 450 97 25 -12.240695 277 277 5 701 450 97 25 -12.240695 278 278 3 702 446 97 22 -16.240695 279 279 0 702 446 97 22 -16.240695 280 280 3 702 446 97 22 -16.240695 281 281 1 702 446 97 22 -16.240695 282 282 2 702 446 97 22 -16.240695 283 283 3 702 446 97 22 -16.240695 284 284 1 704 472 97 36 9.759305 285 285 0 704 472 97 36 9.759305 286 286 4 704 472 97 36 9.759305 287 287 0 704 472 97 36 9.759305 288 288 0 704 472 97 36 9.759305 289 289 0 705 472 97 36 9.759305 290 290 0 706 460 97 30 -2.240695 291 291 0 706 460 97 30 -2.240695 292 292 0 706 460 97 30 -2.240695 293 293 1 708 442 97 19 -20.240695 294 294 3 708 442 97 19 -20.240695 295 295 4 708 442 97 19 -20.240695 296 296 0 708 442 97 19 -20.240695 297 297 4 708 442 97 19 -20.240695 298 298 2 708 442 97 19 -20.240695 299 299 0 709 525 97 62 62.759305 300 300 0 709 525 97 62 62.759305 301 301 1 709 525 97 62 62.759305 302 302 0 710 533 97 63 70.759305 303 303 1 710 533 97 63 70.759305 304 304 2 710 533 97 63 70.759305 305 305 0 710 533 97 63 70.759305 306 306 0 710 533 97 63 70.759305 307 307 0 710 533 97 63 70.759305 308 308 1 711 513 97 57 50.759305 309 309 0 711 513 97 57 50.759305 310 310 0 711 513 97 57 50.759305 311 311 1 711 513 97 57 50.759305 312 312 1 711 513 97 57 50.759305 313 313 0 711 513 97 57 50.759305 314 314 0 711 513 97 57 50.759305 315 315 0 712 514 97 58 51.759305 316 316 0 712 514 97 58 51.759305 317 317 0 713 511 97 56 48.759305 318 318 0 713 511 97 56 48.759305 319 319 1 713 511 97 56 48.759305 320 320 0 713 511 97 56 48.759305 321 321 0 713 511 97 56 48.759305 322 322 0 714 511 97 56 48.759305 323 323 0 714 511 97 56 48.759305 324 324 1 714 511 97 56 48.759305 325 325 0 714 511 97 56 48.759305 326 326 0 714 511 97 56 48.759305 327 327 0 714 511 97 56 48.759305 328 328 0 715 496 97 51 33.759305 329 329 0 715 496 97 51 33.759305 330 330 0 715 496 97 51 33.759305 331 331 0 715 496 97 51 33.759305 332 332 1 716 494 97 49 31.759305 333 333 0 716 494 97 49 31.759305 334 334 0 717 494 97 49 31.759305 335 335 0 717 494 97 49 31.759305 336 336 0 717 494 97 49 31.759305 337 337 2 718 411 97 4 -51.240695 338 338 4 718 411 97 4 -51.240695 339 339 4 718 411 97 4 -51.240695 340 340 1 718 411 97 4 -51.240695 341 341 2 718 411 97 4 -51.240695 342 342 2 719 411 97 4 -51.240695 343 343 1 719 411 97 4 -51.240695 344 344 4 719 411 97 4 -51.240695 345 345 1 719 411 97 4 -51.240695 346 346 3 719 411 97 4 -51.240695 347 347 3 719 411 97 4 -51.240695 348 348 2 720 423 97 10 -39.240695 349 349 0 720 423 97 10 -39.240695 350 350 3 721 424 97 11 -38.240695 351 351 2 722 403 97 1 -59.240695 352 352 0 722 403 97 1 -59.240695 353 353 1 722 403 97 1 -59.240695 354 354 0 723 409 97 2 -53.240695 355 355 3 723 409 97 2 -53.240695 356 356 1 723 409 97 2 -53.240695 357 357 10 724 434 97 16 -28.240695 358 358 4 724 434 97 16 -28.240695 359 359 1 724 434 97 16 -28.240695 360 360 2 725 477 97 39 14.759305 361 361 0 725 477 97 39 14.759305 362 362 1 725 477 97 39 14.759305 363 363 0 727 472 97 36 9.759305 364 364 0 728 468 97 34 5.759305 365 365 0 728 468 97 34 5.759305 366 366 0 729 470 97 35 7.759305 367 367 0 730 486 97 43 23.759305 368 368 0 731 495 97 50 32.759305 369 369 0 731 495 97 50 32.759305 370 370 0 731 495 97 50 32.759305 371 371 0 731 495 97 50 32.759305 372 372 0 731 495 97 50 32.759305 373 373 0 732 483 97 41 20.759305 374 374 0 732 483 97 41 20.759305 375 375 2 732 483 97 41 20.759305 376 376 2 733 442 97 19 -20.240695 377 377 2 733 442 97 19 -20.240695 378 378 0 734 457 97 28 -5.240695 379 379 4 736 457 97 28 -5.240695 380 380 5 736 457 97 28 -5.240695 381 381 2 737 457 97 28 -5.240695 382 382 3 737 457 97 28 -5.240695 383 383 2 737 457 97 28 -5.240695 384 384 2 737 457 97 28 -5.240695 385 385 1 737 457 97 28 -5.240695 386 386 2 737 457 97 28 -5.240695 387 387 0 737 457 97 28 -5.240695 388 388 2 738 464 97 31 1.759305 389 389 0 738 464 97 31 1.759305 390 390 1 738 464 97 31 1.759305 391 391 0 739 433 97 15 -29.240695 392 392 3 739 433 97 15 -29.240695 393 393 1 739 433 97 15 -29.240695 394 394 0 739 433 97 15 -29.240695 395 395 0 740 442 97 19 -20.240695 396 396 0 740 442 97 19 -20.240695 397 397 0 741 433 97 15 -29.240695 398 398 1 741 433 97 15 -29.240695 399 399 0 741 433 97 15 -29.240695 400 400 0 742 430 97 14 -32.240695 401 401 0 742 430 97 14 -32.240695 402 402 2 743 450 97 25 -12.240695 403 403 0 743 450 97 25 -12.240695
One of the areas in which Julia shines is optimization packages. I mostly do nonlinear optimization subject to box constraints and use the NLopt
package. Many other types of optimization problems can be addressed with the JuMP
package.
A recent addition is the JuliaDiff organization that provides several types of automatic differentiation packages for Julia.