


* make groupstats into a class with additional functions for easy access
  use bincount but loop over 2d, or maybe two versions 1d,2d (or nd?)
  similar to groupbys
  class GroupStats
    init with label data
    attributes/properties on demand: mean, var, callback, devfrommean, meanarr, vararr
  does var need bias option ? yes?
  def groupmean(label, data)
  def groupvar(label, data)
  def groupnormalize(label, data, reweight = True)


* ANOVA wrapper usage example
  discussed on mailing list and proposed by Skipper
  create design matrix
  create restriction matrices for F tests, t tests
  ? normalization redundant ?
  reports ANOVA style

* maybe: quick helpers for structured arrays and masked arrays
  formula for selection of variables
  specification for which variable are factors
  Anova(y, use='x1;x2;x3', factors='x2', data = dataarray)
  or
  Anova(y, use='x1; x2:F; x3', data = dataarray)

  masked arrays need row compression


* meta object
  for variable names, and .... ?

* OLSR, OLS with linear restriction    -> design changes ?
  need to overwrite params_cov calculation - move it back to models

* regression: OLS, WLS
  - add prediction


* Granger causality test: which test statistic, F test is easiest, LR, Wald ?  -> easy
  in R: example in Wikipedia

* get lag matrix helper function to use lagged dependent and lagged independent regressors

* nonlinear hypothesis tests for the estimate parameters, delta method ?  -> easy, but derivatives by hand w/o sympy

* minimal PCA, PCR, or PLS (NIPALS): interesting also for finance but not urgent -> messy multiplicity of definitions ?

* non-linear least squares:
  use/imitate scipy.interpolate curvefit + full set of results statistics

* tests    -> needed
  - GLSAR: check, R's gls
  - ARMA: check R for arma, e.g. dynamo, also look more closely at GARCH_UCSD (BSD) and offspring (license ?)

* other models in draft stage  -> requires cleaning
  - gaussian process   -> might fit in
  - multinomial logit -> requires ML, result statistics don't fit into current classes ?

* stochastic processes, time series  -> first step is relatively easy
  more simulators, random process generators, for fun and Monte Carlo and testing
  estimators to follow 
  first group GARCH, 
  continuous time: not until I need them

* other multivariate analysis
  discriminance, factor analysis, more anova: not my business

* MLE, GMM: big open question  -> needs to wait until we have more code that uses it
  difference of approaches
  - parametric assumptions, distributions fully specified - problem misspecification
  - efficient estimation with full information MLE
  -> example panel data, yogurt paper
