Clarify or check Estimator input shapes

I was trying to run one of the Estimators using weighted_least_squares without initializing a Dataset and was getting confusing errors from numpy.einsum before I realized that inputs need to be 2D no matter what. We can coerce 1D inputs to 2D with a new Estimator._validate_inputs() method, or we can just update the docstrings to clarify requirements.

BTW, based on variable convention, I think it's reasonable to assume that X must be 2D, but it's not obvious that y, v, etc. should be 2D as well, and indeed, there's an obvious error about shape if X is 1D, but no shape check for y, v, etc.

To replicate:

y = np.random.random(10)
v = np.random.random(10) ** 2
X = np.random.random((10, 1))
est = pymare.estimators.DerSimonianLaird()
est.fit(y=y, v=v, X=X)
print(est.results.to_df())

Results in:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-41d64261b276> in <module>()
      3 X = np.random.random((10, 1))
      4 est = pymare.estimators.DerSimonianLaird()
----> 5 est.fit(y=y, v=v, X=X)
      6 print(est.results.to_df())

~/Documents/tsalo/PyMARE/pymare/estimators/estimators.py in fit(self, dataset, **kwargs)
     78                     kwargs[name] = getattr(dataset, name)
     79 
---> 80         self.params_ = self._fit(**kwargs)
     81         self.dataset_ = dataset
     82 

~/Documents/tsalo/PyMARE/pymare/estimators/estimators.py in _fit(self, y, v, X)
    180 
    181         # Estimate initial betas with WLS, assuming tau^2=0
--> 182         beta_wls, inv_cov = weighted_least_squares(y, v, X, return_cov=True)
    183 
    184         # Cochrane's Q

~/Documents/tsalo/PyMARE/pymare/stats.py in weighted_least_squares(y, v, X, tau2, return_cov)
     24 
     25     # Einsum indices: k = studies, p = predictors, i = parallel iterates
---> 26     wX = np.einsum('kp,ki->ipk', X, w)
     27     cov = wX.dot(X)
     28 

<__array_function__ internals> in einsum(*args, **kwargs)

~/anaconda/envs/python3/lib/python3.6/site-packages/numpy/core/einsumfunc.py in einsum(*operands, **kwargs)
   1354     # If no optimization, run pure einsum
   1355     if optimize_arg is False:
-> 1356         return c_einsum(*operands, **kwargs)
   1357 
   1358     valid_einsum_kwargs = ['out', 'dtype', 'order', 'casting']

ValueError: einstein sum subscripts string contains too many subscripts for operand 1

Reproduce the example and start in pymare/estimators/estimators.py, following fit into the estimator implementation and pymare/stats.py's weighted_least_squares. Determine the intended handling of 1D y and v versus X, then ensure the chosen validation or documentation makes those requirements explicit and avoids the confusing einsum failure.