Merge pull request #17 from scidom/master

Added integrated autocorrelation time, effective sample size, MCMC variance estimators (and temporarily ported acf functions)
Merge pull request #18 from scidom/master

Debugged actime and ess (now working)
Fixed a bug and improved interface in MCMC stats (ess, actime and var)
Added MCMC variance initial positive sequence estimator (IPSE)
Fix logit link and add const to global objects

The inverse of the logit link should have a negative sign in the
exponent.  This is present in other examples but not in these two.

It might be a good idea to contrast the MCMC results, at least in the
documentation, with those from fitting a GLM.  Right now it is kind of
clumsy to fit a GLM with a given model matrix but that could be
changed.

With the data from `example/binomial.jl` and the definition of the coefficients
```jl
julia> m = model(ex, vars=zeros(nbeta), gradient=true)
MCMCLikelihoodModel(##ll#319,# function,nothing,nothing,##ll#340,nothing,nothing,[:vars=>PDims(1,(10,))],10,[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0])

julia> res = run(m * RWM(0.05), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 4.1 sec.

julia> res = run(m * RWM(0.05), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 3.3 sec.

julia> head(res.samples)
6x10 DataFrame:
           vars.1    vars.2     vars.3    vars.4   vars.5  vars.6    vars.7   vars.8    vars.9   vars.10
[1,]     -0.21506 -0.443009 -0.0923568 -0.765506 -1.50518 1.45962 -0.166601 -1.21317 -0.510049 -0.470377
[2,]    -0.180667 -0.460005 -0.0881983 -0.730407 -1.50652 1.42352 -0.257553 -1.22122 -0.427078 -0.489092
[3,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[4,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[5,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487
[6,]    -0.172506 -0.516425 -0.0515133 -0.704989 -1.46919 1.46088 -0.209047 -1.23566 -0.464969 -0.438487

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.21482366575183443]   -0.0717078
 [-0.4725536125628145]    -0.250912
 [-0.020901613339271534]  -0.100819
 [-0.5815990199432688]    -0.512176
 [-1.405229813825649]     -1.19319
 [1.3544778459403048]      1.22552
 [-0.2580605077816288]    -0.246421
 [-1.0614956381864762]    -1.00204
 [-0.423581899678947]     -0.59563
 [-0.5593022590885337]    -0.681701

julia> res = run(m * HMC(2, 0.1), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 10.0 sec.

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.2046847981894676]    -0.0717078
 [-0.462927351516865]     -0.250912
 [-0.024959659831684532]  -0.100819
 [-0.5805002542280536]    -0.512176
 [-1.413034972454002]     -1.19319
 [1.358553312357889]       1.22552
 [-0.2584049999023584]    -0.246421
 [-1.058018576520552]     -1.00204
 [-0.42451432264431094]   -0.59563
 [-0.5546427132866298]    -0.681701

julia> res = run(m * NUTS(), steps=1000:10000)
10 parameters, 9001 samples (per parameter), 17.4 sec.

julia> [colwise(mean, res.samples) beta0 ] # mean sample vs original coefs
10x2 Array{Any,2}:
 [-0.20488317740629458]   -0.0717078
 [-0.465418897468125]     -0.250912
 [-0.024554388672484415]  -0.100819
 [-0.5822459997386241]    -0.512176
 [-1.4147503896216957]    -1.19319
 [1.3579861510776314]      1.22552
 [-0.2573139709672702]    -0.246421
 [-1.0575141534744146]    -1.00204
 [-0.42458208644443585]   -0.59563
 [-0.5552305354105672]    -0.681701

julia> fr = DataFrame(y = Y,x1=X[:,2],x2=X[:,3],x3=X[:,4],x4=X[:,5],x5=X[:,6],x6=X[:,7],x7=X[:,8],x8=X[:,9],x9=X[:,10]);

julia> using GLM

julia> glm(:(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9), fr, Binomial())

Formula: y ~ :(+(x1,x2,x3,x4,x5,x6,x7,x8,x9))

Coefficients:

10x4 DataFrame:
           Estimate Std.Error   z value    Pr(>|z|)
[1,]      -0.206247 0.0878745  -2.34707   0.0189218
[2,]      -0.467542 0.0924892   -5.0551  4.30171e-7
[3,]     -0.0246491 0.0844057 -0.292031    0.770263
[4,]       -0.58779 0.0903566  -6.50522 7.75793e-11
[5,]        -1.4265  0.117335  -12.1575 5.23478e-34
[6,]        1.37016  0.112457   12.1839 3.78862e-34
[7,]      -0.259542 0.0864448   -3.0024  0.00267858
[8,]       -1.06695  0.105179  -10.1441 3.51962e-24
[9,]      -0.430233 0.0900796  -4.77614  1.78689e-6
[10,]      -0.56062 0.0927221  -6.04624  1.48267e-9
```
Merge pull request #19 from JuliaStats/db/binomial

Fix logit link and add const to global objects
Added Monte Carlo standard error estimator using batch means (not exported
yet)
Exported MC standard error estimator based on batch means + various improvements in var, ess
Added position-dependent MALA (PMALA) sampler
Added mean method for MCMCChain
Simplified mean and var_iid definitions for MCMCChain
Added summary stats (acceptance rate and describe methods, see issue 22)
Revamped ess, actime, var, std to work with data vectors too, made describe method for chains more informative, updated README (this completes a first round of MCMC summary statistics
Organized examples
Improvements in MCMC variance coding
Split RAM from RWM and deprecated MMALA (succeeded by PMALA)
Split RAM from RWM and deprecated MMALA (succeeded by PMALA)