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Update py-statsmodels to 0.8.0rc1:
Module Name: pkgsrc-wip
Committed By: Thomas Klausner <wiz%NetBSD.org@localhost>
Pushed By: wiz
Date: Thu Jul 14 09:26:46 2016 +0200
Changeset: 90443a6f88e357e0ed2b2512711b3be8e9221261
Modified Files:
py-statsmodels/Makefile
py-statsmodels/PLIST
py-statsmodels/distinfo
Log Message:
Update py-statsmodels to 0.8.0rc1:
===========
0.8 Release
===========
Release 0.8.0
=============
See also changes in the unreleased 0.7
Release summary
---------------
The main features of this release are several new time series models based
on the statespace framework, multiple imputation using MICE as well as many
other enhancements. The codebase also has been updated to be compatible with
recent numpy and pandas releases.
Statsmodels is using now github to store the updated documentation which
is available under
http://www.statsmodels.org/stable for the last release, and
http://www.statsmodels.org/dev/ for the development version.
This is the last release that supports Python 2.6.
**Warning**
API stability is not guaranteed for new features, although even in this case
changes will be made in a backwards compatible way if possible. The stability
of a new feature depends on how much time it was already in statsmodels master
and how much usage it has already seen.
If there are specific known problems or limitations, then they are mentioned
in the docstrings.
The following major new features appear in this version.
Statespace Models
-----------------
Building on the statespace framework and models added in 0.7, this release
includes additional models that build on it.
Authored by Chad Fulton largely during GSOC 2015
Kalman Smoother
^^^^^^^^^^^^^^^
The Kalman smoother (introduced in #2434) allows making inference on the
unobserved state vector at each point in time using data from the entire
sample. In addition to this improved inference, the Kalman smoother is required
for future improvements such as simulation smoothing and the expectation
maximization (EM) algorithm.
As a result of this improvement, all state space models now inherit a `smooth`
method for producing results with smoothed state estimates. In addition, the
`fit` method will return results with smoothed estimates at the maximum
likelihood estimates.
Postestimation
^^^^^^^^^^^^^^
Improved post-estimation output is now available to all state space models
(introduced in #2566). This includes the new methods `get_prediction` and
`get_forecast`, providing standard errors and confidence intervals as well
as point estimates, `simulate`, providing simulation of time series following
the given state space process, and `impulse_responses`, allowing computation
of impulse responses due to innovations to the state vector.
Diagnostics
^^^^^^^^^^^
A number of general diagnostic tests on the residuals from state space
estimation are now available to all state space models (introduced in #2431).
These include:
* `test_normality` implements the Jarque-Bera test for normality of residuals
* `test_heteroskedasticity` implements a test for homoskedasticity of
residuals similar to the Goldfeld-Quandt test
* `test_serial_correlation` implements the Ljung-Box (or Box-Pierce) test for
serial correlation of residuals
These test statistics are also now included in the `summary` method output. In
addition, a `plot_diagnostics` method is available which provides four plots
to visually assess model fit.
Unobserved Components
^^^^^^^^^^^^^^^^^^^^^
The class of univariate Unobserved Components models (also known as structural
time series models) are now available (introduced in #2432). This includes as
special cases the local level model and local linear trend model. Generically
it allows decomposing a time series into trend, cycle, seasonal, and
irregular components, optionally with exogenous regressors and / or
autoregressive errors.
Multivariate Models
^^^^^^^^^^^^^^^^^^^
Two standard multivariate econometric models - vector autoregressive
moving-average model with exogenous regressors (VARMAX) and Dynamic Factors
models - are now available (introduced in #2563). The first is a popular
reduced form method of exploring the covariance in several time series, and the
second is a popular reduced form method of extracting a small number of common
factors from a large dataset of observed series.
Recursive least squares
^^^^^^^^^^^^^^^^^^^^^^^
A model for recursive least squares, also known as expanding-window OLS, is
now available in `statsmodels.regression` (introduced in #2830).
Miscellaneous
^^^^^^^^^^^^^
Other improvements to the state space framework include:
* Improved missing data handling #2770, #2809
* Ongoing refactoring and bug fixes in fringes and corner cases
New functionality in statistics
-------------------------------
Contingency Tables #2418 (Kerby Shedden)
Local FDR, multiple testing #2297 (Kerby Shedden)
Mediation Analysis #2352 (Kerby Shedden)
other:
* weighted quantiles in DescrStatsW #2707 (Kerby Shedden)
Duration
--------
Kaplan Meier Survival Function #2614 (Kerby Shedden)
Cumulative incidence rate function #3016 (Kerby Shedden)
other:
* frequency weights in Kaplan-Meier #2992 (Kerby Shedden)
Imputation
----------
new subpackage in `statsmodels.imputation`
MICE #2076 (Frank Cheng GSOC 2014 and Kerby Shedden)
Imputation by regression on Order Statistic #3019 (Paul Hobson)
Time Series Analysis
--------------------
Markov Switching Models
^^^^^^^^^^^^^^^^^^^^^^^
Markov switching dynamic regression and autoregression models are now
available (introduced in #2980 by Chad Fulton). These models allow regression
effects and / or autoregressive dynamics to differ depending on an unobserved
"regime"; in Markov switching models, the regimes are assumed to transition
according to a Markov process.
Statistics
^^^^^^^^^^
* KPSS stationarity, unit root test #2775 (N-Wouda)
* The Brock Dechert Scheinkman (BDS) test for nonlinear dependence is now
available (introduced in #934 by Chad Fulton)
Penalized Estimation
--------------------
Elastic net: fit_regularized with L1/L2 penalization has been added to
OLS, GLM and PHReg (Kerby Shedden)
GLM
---
Tweedie is now available as new family #2872 (Peter Quackenbush, Josef Perktold)
other:
* frequency weights for GLM (currently without full support) #
* more flexible convergence options #2803 (Peter Quackenbush)
Multivariate
------------
new subpackage that currently contains PCA
PCA was added in 0.7 to statsmodels.tools and is now in statsmodels.multivariate
Documentation
-------------
New doc build with latest jupyter and Python 3 compatibility (Tom Augspurger)
Other important improvements
----------------------------
several existing functions have received improvements
* seasonal_decompose: improved periodicity handling #2987 (ssktotoro ?)
* tools add_constant, add_trend: refactoring and pandas compatibility #2240 (Kevin Sheppard)
* acf, pacf, acovf: option for missing handling #3020 (joesnacks ?)
* acf, pacf plots: allow array of lags #2989 (Kevin Sheppard)
* io SimpleTable (summary): allow names with special characters #3015 (tvanessa ?)
* tsa tools lagmat, lagmat2ds: pandas support #2310 #3042 (Kevin Sheppard)
* CompareMeans: from_data, summary methods #2754 (Valery Tyumen)
Major Bugs fixed
----------------
* see github issues
Backwards incompatible changes and deprecations
-----------------------------------------------
* ???
* predict now returns a pandas Series if the exog argument is a DataFrame
* PCA moved to multivariate compared to 0.7
Release 0.7.0
=============
Release summary
---------------
**Note:** This version has never been officially released. Several models have
been refactored, improved or bugfixed in 0.8.
The following major new features appear in this version.
Principal Component Analysis
----------------------------
Author: Kevin Sheppard
A new class-based Principal Component Analysis has been added. This
class replaces the function-based PCA that previously existed in the
sandbox. This change bring a number of new features, including:
* Options to control the standardization (demeaning/studentizing)
* Scree plotting
* Information criteria for selecting the number of factors
* R-squared plots to assess component fit
* NIPALS implementation when only a small number of components are required and the dataset is large
* Missing-value filling using the EM algorithm
.. code-block:: python
import statsmodels.api as sm
from statsmodels.tools.pca import PCA
data = sm.datasets.fertility.load_pandas().data
columns = map(str, range(1960, 2012))
data.set_index('Country Name', inplace=True)
dta = data[columns]
dta = dta.dropna()
pca_model = PCA(dta.T, standardize=False, demean=True)
pca_model.plot_scree()
*Note* : A function version is also available which is compatible with the
call in the sandbox. The function version is just a thin wrapper around the
class-based PCA implementation.
Regression graphics for GLM/GEE
-------------------------------
Author: Kerby Shedden
Added variable plots, partial residual plots, and CERES residual plots
are available for GLM and GEE models by calling the methods
`plot_added_variable`, `plot_partial_residuals`, and
`plot_ceres_residuals` that are attached to the results classes.
State Space Models
------------------
Author: Chad Fulton
State space methods provide a flexible structure for the estimation and
analysis of a wide class of time series models. The Statsmodels implementation
allows specification of state models, fast Kalman filtering, and built-in
methods to facilitate maximum likelihood estimation of arbitrary models. One of
the primary goals of this module is to allow end users to create and estimate
their own models. Below is a short example demonstrating the ease with which a
local level model can be specified and estimated:
.. code-block:: python
import numpy as np
import statsmodels.api as sm
import pandas as pd
data = sm.datasets.nile.load_pandas().data
data.index = pd.DatetimeIndex(data.year.astype(int).astype(str), freq='AS')
# Setup the state space representation
class LocalLevel(sm.tsa.statespace.MLEModel):
def __init__(self, endog):
# Initialize the state space model
super(LocalLevel, self).__init__(
endog, k_states=1, initialization='approximate_diffuse')
# Setup known components of state space representation matrices
self.ssm['design', :] = 1.
self.ssm['transition', :] = 1.
self.ssm['selection', :] = 1.
# Describe how parameters enter the model
def update(self, params, transformed=True):
params = super(LocalLevel, self).update(params, transformed)
self.ssm['obs_cov', 0, 0] = params[0]
self.ssm['state_cov', 0, 0] = params[1]
def transform_params(self, params):
return params**2 # force variance parameters to be positive
# Specify start parameters and parameter names
@property
def start_params(self):
return [np.std(self.endog)]*2
@property
def param_names(self):
return ['sigma2.measurement', 'sigma2.level']
# Fit the model with maximum likelihood estimation
mod = LocalLevel(data['volume'])
res = mod.fit()
print res.summary()
The documentation and example notebooks provide further examples of how to
form state space models. Included in this release is a full-fledged
model making use of the state space infrastructure to estimate SARIMAX
models. See below for more details.
Time Series Models (ARIMA) with Seasonal Effects
------------------------------------------------
Author: Chad Fulton
A model for estimating seasonal autoregressive integrated moving average models
with exogenous regressors (SARIMAX) has been added by taking advantage of the
new state space functionality. It can be used very similarly to the existing
`ARIMA` model, but works on a wider range of specifications, including:
* Additive and multiplicative seasonal effects
* Flexible trend specications
* Regression with SARIMA errors
* Regression with time-varying coefficients
* Measurement error in the endogenous variables
Below is a short example fitting a model with a number of these components,
including exogenous data, a linear trend, and annual multiplicative seasonal
effects.
.. code-block:: python
import statsmodels.api as sm
import pandas as pd
data = sm.datasets.macrodata.load_pandas().data
data.index = pd.DatetimeIndex(start='1959-01-01', end='2009-09-01',
freq='QS')
endog = data['realcons']
exog = data['m1']
mod = sm.tsa.SARIMAX(endog, exog=exog, order=(1,1,1),
trend='t', seasonal_order=(0,0,1,4))
res = mod.fit()
print res.summary()
Generalized Estimating Equations GEE
------------------------------------
Author: Kerby Shedden
Enhancements and performance improvements for GEE:
* EquivalenceClass covariance structure allows covariances to be specified by
arbitrary collections of equality constraints #2188
* add weights #2090
* refactored margins #2158
MixedLM
-------
Author: Kerby Shedden with Saket Choudhary
Enhancements to MixedLM (#2363): added variance components support for
MixedLM allowing a wider range of random effects structures to be specified;
also performance improvements from use of sparse matrices internally for
random effects design matrices.
Other important new features
----------------------------
* GLM: add scipy-based gradient optimization to fit #1961 (Kerby Shedden)
* wald_test_terms: new method of LikelihoodModels to compute wald tests (F or chi-square)
for terms or sets of coefficients #2132 (Josef Perktold)
* add cov_type with fixed scale in WLS to allow chi2-fitting #2137 #2143
(Josef Perktold, Christoph Deil)
* VAR: allow generalized IRF and FEVD computation #2067 (Josef Perktold)
* get_prediction new method for full prediction results (new API convention)
Major Bugs fixed
----------------
* see github issues for a full list
* bug in ARMA/ARIMA predict with `exog` #2470
* bugs in VAR
* x13: python 3 compatibility
===========
0.6 Release
===========
Release 0.6.1
=============
Statsmodels 0.6.1 is a bugfix release. All users are encouraged to upgrade to 0.6.1.
See the :ref:`list of fixed issues <issues_list_06>` for specific backported fixes.
Release 0.6.0
=============
Statsmodels 0.6.0 is another large release. It is the result of the work of 37 authors over the last year and includes over 1500 commits. It contains many new features, improvements, and bug fixes detailed below.
See the :ref:`list of fixed issues <issues_list_06>` for specific closed issues.
The following major new features appear in this version.
Generalized Estimating Equations
--------------------------------
Generalized Estimating Equations (GEE) provide an approach to handling
dependent data in a regression analysis. Dependent data arise
commonly in practice, such as in a longitudinal study where repeated
observations are collected on subjects. GEE can be viewed as an
extension of the generalized linear modeling (GLM) framework to the
dependent data setting. The familiar GLM families such as the
Gaussian, Poisson, and logistic families can be used to accommodate
dependent variables with various distributions.
Here is an example of GEE Poisson regression in a data set with four
count-type repeated measures per subject, and three explanatory
covariates.
.. code-block:: python
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
data = sm.datasets.get_rdataset("epil", "MASS").data
md = smf.gee("y ~ age + trt + base", "subject", data,
cov_struct=sm.cov_struct.Independence(),
family=sm.families.Poisson())
mdf = md.fit()
print mdf.summary()
The dependence structure in a GEE is treated as a nuisance parameter
and is modeled in terms of a "working dependence structure". The
statsmodels GEE implementation currently includes five working
dependence structures (independent, exchangeable, autoregressive,
nested, and a global odds ratio for working with categorical data).
Since the GEE estimates are not maximum likelihood estimates,
alternative approaches to some common inference procedures have been
developed. The statsmodels GEE implementation currently provides
standard errors, Wald tests, score tests for arbitrary parameter
contrasts, and estimates and tests for marginal effects. Several
forms of standard errors are provided, including robust standard
errors that are approximately correct even if the working dependence
structure is misspecified.
Seasonality Plots
-----------------
Adding functionality to look at seasonality in plots. Two new functions are :func:`sm.graphics.tsa.month_plot` and :func:`sm.graphics.tsa.quarter_plot`. Another function :func:`sm.graphics.tsa.seasonal_plot` is available for power users.
.. code-block:: python
import statsmodels.api as sm
import pandas as pd
dta = sm.datasets.elnino.load_pandas().data
dta['YEAR'] = dta.YEAR.astype(int).astype(str)
dta = dta.set_index('YEAR').T.unstack()
dates = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
dta.index.values)
dta.index = pd.DatetimeIndex(dates, freq='M')
fig = sm.tsa.graphics.month_plot(dta)
.. currentmodule:: statsmodels.tsa
Seasonal Decomposition
----------------------
We added a naive seasonal decomposition tool in the same vein as R's ``decompose``. This function can be found as :func:`sm.tsa.seasonal_decompose <tsa.seasonal.seasonal_decompose>`.
.. plot::
:include-source:
import statsmodels.api as sm
dta = sm.datasets.co2.load_pandas().data
# deal with missing values. see issue
dta.co2.interpolate(inplace=True)
res = sm.tsa.seasonal_decompose(dta.co2)
res.plot()
Addition of Linear Mixed Effects Models (MixedLM)
Linear Mixed Effects Models
---------------------------
Linear Mixed Effects models are used for regression analyses involving
dependent data. Such data arise when working with longitudinal and
other study designs in which multiple observations are made on each
subject. Two specific mixed effects models are "random intercepts
models", where all responses in a single group are additively shifted
by a value that is specific to the group, and "random slopes models",
where the values follow a mean trajectory that is linear in observed
covariates, with both the slopes and intercept being specific to the
group. The Statsmodels MixedLM implementation allows arbitrary random
effects design matrices to be specified for the groups, so these and
other types of random effects models can all be fit.
Here is an example of fitting a random intercepts model to data from a
longitudinal study:
.. code-block:: python
import statsmodels.api as sm
import statsmodels.formula.api as smf
data = sm.datasets.get_rdataset('dietox', 'geepack', cache=True).data
md = smf.mixedlm("Weight ~ Time", data, groups=data["Pig"])
mdf = md.fit()
print mdf.summary()
The Statsmodels LME framework currently supports post-estimation
inference via Wald tests and confidence intervals on the coefficients,
profile likelihood analysis, likelihood ratio testing, and AIC. Some
limitations of the current implementation are that it does not support
structure more complex on the residual errors (they are always
homoscedastic), and it does not support crossed random effects. We
hope to implement these features for the next release.
Wrapping X-12-ARIMA/X-13-ARIMA
------------------------------
It is now possible to call out to X-12-ARIMA or X-13ARIMA-SEATS from statsmodels. These libraries must be installed separately.
.. plot::
:include-source:
import statsmodels.api as sm
dta = sm.datasets.co2.load_pandas().data
dta.co2.interpolate(inplace=True)
dta = dta.resample('M').last()
res = sm.tsa.x13_arima_select_order(dta.co2)
print(res.order, res.sorder)
results = sm.tsa.x13_arima_analysis(dta.co2)
fig = results.plot()
fig.set_size_inches(12, 5)
fig.tight_layout()
Other important new features
----------------------------
* The AR(I)MA models now have a :func:`plot_predict <arima_model.ARMAResults.plot_predict>` method to plot forecasts and confidence intervals.
* The Kalman filter Cython code underlying AR(I)MA estimation has been substantially optimized. You can expect speed-ups of one to two orders of magnitude.
* Added :func:`sm.tsa.arma_order_select_ic`. A convenience function to quickly get the information criteria for use in tentative order selection of ARMA processes.
* Plotting functions for timeseries is now imported under the ``sm.tsa.graphics`` namespace in addition to ``sm.graphics.tsa``.
* New `distributions.ExpandedNormal` class implements the Edgeworth expansion for weakly non-normal distributions.
* **New datasets**: Added new :ref:`datasets <datasets>` for examples. ``sm.datasets.co2`` is a univariate time-series dataset of weekly co2 readings. It exhibits a trend and seasonality and has missing values.
* Added robust skewness and kurtosis estimators in :func:`sm.stats.stattools.robust_skewness` and :func:`sm.stats.stattools.robust_kurtosis`, respectively. An alternative robust measure of skewness has been added in :func:`sm.stats.stattools.medcouple`.
* New functions added to correlation tools: `corr_nearest_factor`
finds the closest factor-structured correlation matrix to a given
square matrix in the Frobenius norm; `corr_thresholded` efficiently
constructs a hard-thresholded correlation matrix using sparse matrix
operations.
* New `dot_plot` in graphics: A dotplot is a way to visualize a small dataset
in a way that immediately conveys the identity of every point in the plot.
Dotplots are commonly seen in meta-analyses, where they are known
as "forest plots", but can be used in many other settings as well.
Most tables that appear in research papers can be represented
graphically as a dotplot.
* Statsmodels has added custom warnings to ``statsmodels.tools.sm_exceptions``. By default all of these warnings will be raised whenever appropriate. Use ``warnings.simplefilter`` to turn them off, if desired.
* Allow control over the namespace used to evaluate formulas with patsy via the ``eval_env`` keyword argument. See the :ref:`patsy-namespaces` documentation for more information.
Major Bugs fixed
----------------
* NA-handling with formulas is now correctly handled. :ghissue:`805`, :ghissue:`1877`.
* Better error messages when an array with an object dtype is used. :ghissue:`2013`.
* ARIMA forecasts were hard-coded for order of integration with ``d = 1``. :ghissue:`1562`.
.. currentmodule:: statsmodels.tsa
Backwards incompatible changes and deprecations
-----------------------------------------------
* RegressionResults.norm_resid is now a readonly property, rather than a function.
* The function ``statsmodels.tsa.filters.arfilter`` has been removed. This did not compute a recursive AR filter but was instead a convolution filter. Two new functions have been added with clearer names :func:`sm.tsa.filters.recursive_filter <tsa.filters.filtertools.recursive_filter>` and :func:`sm.tsa.filters.convolution_filter <tsa.filters.filtertools.convolution_filter>`.
To see a diff of this commit:
https://wip.pkgsrc.org/cgi-bin/gitweb.cgi?p=pkgsrc-wip.git;a=commitdiff;h=90443a6f88e357e0ed2b2512711b3be8e9221261
Please note that diffs are not public domain; they are subject to the
copyright notices on the relevant files.
diffstat:
py-statsmodels/Makefile | 5 +-
py-statsmodels/PLIST | 1035 ++++++++++++++++++++++++++---------------------
py-statsmodels/distinfo | 7 +-
3 files changed, 580 insertions(+), 467 deletions(-)
diffs:
diff --git a/py-statsmodels/Makefile b/py-statsmodels/Makefile
index e41af50..7011798 100644
--- a/py-statsmodels/Makefile
+++ b/py-statsmodels/Makefile
@@ -1,6 +1,6 @@
# $NetBSD: Makefile,v 1.1 2014/07/26 11:16:19 jihbed Exp $
-DISTNAME= statsmodels-0.5.0
+DISTNAME= statsmodels-0.8.0rc1
PKGNAME= ${PYPKGPREFIX}-${DISTNAME}
CATEGORIES= math
MASTER_SITES= ${MASTER_SITE_PYPI:=s/statsmodels/}
@@ -10,13 +10,10 @@ HOMEPAGE= http://statsmodels.sourceforge.net/
COMMENT= Statistical computations and models for use with SciPy
LICENSE= modified-bsd
-
DEPENDS+= ${PYPKGPREFIX}-pandas>=0.14.1:../../math/py-pandas
DEPENDS+= ${PYPKGPREFIX}-scipy>=0.12.0:../../math/py-scipy
DEPENDS+= ${PYPKGPREFIX}-patsy>=0.3.0:../../wip/py-patsy
-WRKSRC= ${WRKDIR}/statsmodels-0.5.0
-
.include "../../devel/py-cython/buildlink3.mk"
.include "../../lang/python/egg.mk"
.include "../../math/py-numpy/buildlink3.mk"
diff --git a/py-statsmodels/PLIST b/py-statsmodels/PLIST
index 9c0bd50..831bfed 100644
--- a/py-statsmodels/PLIST
+++ b/py-statsmodels/PLIST
@@ -1,11 +1,10 @@
-@comment $NetBSD: PLIST,v 1.1 2014/07/26 11:16:19 jihbed Exp $
+@comment $NetBSD$
${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
${PYSITELIB}/${EGG_INFODIR}/not-zip-safe
+${PYSITELIB}/${EGG_INFODIR}/requires.txt
${PYSITELIB}/${EGG_INFODIR}/top_level.txt
-${PYSITELIB}/statsmodels/LICENSE.txt
-${PYSITELIB}/statsmodels/TODO.txt
${PYSITELIB}/statsmodels/__init__.py
${PYSITELIB}/statsmodels/__init__.pyc
${PYSITELIB}/statsmodels/__init__.pyo
@@ -15,9 +14,21 @@ ${PYSITELIB}/statsmodels/api.pyo
${PYSITELIB}/statsmodels/base/__init__.py
${PYSITELIB}/statsmodels/base/__init__.pyc
${PYSITELIB}/statsmodels/base/__init__.pyo
+${PYSITELIB}/statsmodels/base/_constraints.py
+${PYSITELIB}/statsmodels/base/_constraints.pyc
+${PYSITELIB}/statsmodels/base/_constraints.pyo
+${PYSITELIB}/statsmodels/base/_penalties.py
+${PYSITELIB}/statsmodels/base/_penalties.pyc
+${PYSITELIB}/statsmodels/base/_penalties.pyo
+${PYSITELIB}/statsmodels/base/covtype.py
+${PYSITELIB}/statsmodels/base/covtype.pyc
+${PYSITELIB}/statsmodels/base/covtype.pyo
${PYSITELIB}/statsmodels/base/data.py
${PYSITELIB}/statsmodels/base/data.pyc
${PYSITELIB}/statsmodels/base/data.pyo
+${PYSITELIB}/statsmodels/base/elastic_net.py
+${PYSITELIB}/statsmodels/base/elastic_net.pyc
+${PYSITELIB}/statsmodels/base/elastic_net.pyo
${PYSITELIB}/statsmodels/base/l1_cvxopt.py
${PYSITELIB}/statsmodels/base/l1_cvxopt.pyc
${PYSITELIB}/statsmodels/base/l1_cvxopt.pyo
@@ -30,12 +41,18 @@ ${PYSITELIB}/statsmodels/base/l1_solvers_common.pyo
${PYSITELIB}/statsmodels/base/model.py
${PYSITELIB}/statsmodels/base/model.pyc
${PYSITELIB}/statsmodels/base/model.pyo
+${PYSITELIB}/statsmodels/base/optimizer.py
+${PYSITELIB}/statsmodels/base/optimizer.pyc
+${PYSITELIB}/statsmodels/base/optimizer.pyo
${PYSITELIB}/statsmodels/base/tests/__init__.py
${PYSITELIB}/statsmodels/base/tests/__init__.pyc
${PYSITELIB}/statsmodels/base/tests/__init__.pyo
${PYSITELIB}/statsmodels/base/tests/test_data.py
${PYSITELIB}/statsmodels/base/tests/test_data.pyc
${PYSITELIB}/statsmodels/base/tests/test_data.pyo
+${PYSITELIB}/statsmodels/base/tests/test_generic_methods.py
+${PYSITELIB}/statsmodels/base/tests/test_generic_methods.pyc
+${PYSITELIB}/statsmodels/base/tests/test_generic_methods.pyo
${PYSITELIB}/statsmodels/base/tests/test_optimize.py
${PYSITELIB}/statsmodels/base/tests/test_optimize.pyc
${PYSITELIB}/statsmodels/base/tests/test_optimize.pyo
@@ -45,38 +62,42 @@ ${PYSITELIB}/statsmodels/base/tests/test_shrink_pickle.pyo
${PYSITELIB}/statsmodels/base/wrapper.py
${PYSITELIB}/statsmodels/base/wrapper.pyc
${PYSITELIB}/statsmodels/base/wrapper.pyo
-${PYSITELIB}/statsmodels/compatnp/__init__.py
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${PYSITELIB}/statsmodels/datasets/__init__.py
${PYSITELIB}/statsmodels/datasets/__init__.pyc
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@@ -87,7 +108,6 @@ ${PYSITELIB}/statsmodels/datasets/anes96/anes96.csv
${PYSITELIB}/statsmodels/datasets/anes96/data.py
${PYSITELIB}/statsmodels/datasets/anes96/data.pyc
${PYSITELIB}/statsmodels/datasets/anes96/data.pyo
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${PYSITELIB}/statsmodels/datasets/cancer/__init__.py
${PYSITELIB}/statsmodels/datasets/cancer/__init__.pyc
${PYSITELIB}/statsmodels/datasets/cancer/__init__.pyo
@@ -95,7 +115,6 @@ ${PYSITELIB}/statsmodels/datasets/cancer/cancer.csv
${PYSITELIB}/statsmodels/datasets/cancer/data.py
${PYSITELIB}/statsmodels/datasets/cancer/data.pyc
${PYSITELIB}/statsmodels/datasets/cancer/data.pyo
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${PYSITELIB}/statsmodels/datasets/ccard/__init__.py
${PYSITELIB}/statsmodels/datasets/ccard/__init__.pyc
${PYSITELIB}/statsmodels/datasets/ccard/__init__.pyo
@@ -103,9 +122,20 @@ ${PYSITELIB}/statsmodels/datasets/ccard/ccard.csv
${PYSITELIB}/statsmodels/datasets/ccard/data.py
${PYSITELIB}/statsmodels/datasets/ccard/data.pyc
${PYSITELIB}/statsmodels/datasets/ccard/data.pyo
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${PYSITELIB}/statsmodels/datasets/committee/__init__.py
${PYSITELIB}/statsmodels/datasets/committee/__init__.pyc
${PYSITELIB}/statsmodels/datasets/committee/__init__.pyo
@@ -113,7 +143,6 @@ ${PYSITELIB}/statsmodels/datasets/committee/committee.csv
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${PYSITELIB}/statsmodels/datasets/committee/data.pyc
${PYSITELIB}/statsmodels/datasets/committee/data.pyo
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${PYSITELIB}/statsmodels/datasets/copper/__init__.py
${PYSITELIB}/statsmodels/datasets/copper/__init__.pyc
${PYSITELIB}/statsmodels/datasets/copper/__init__.pyo
@@ -121,8 +150,6 @@ ${PYSITELIB}/statsmodels/datasets/copper/copper.csv
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${PYSITELIB}/statsmodels/datasets/copper/data.pyc
${PYSITELIB}/statsmodels/datasets/copper/data.pyo
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${PYSITELIB}/statsmodels/datasets/cpunish/__init__.py
${PYSITELIB}/statsmodels/datasets/cpunish/__init__.pyc
${PYSITELIB}/statsmodels/datasets/cpunish/__init__.pyo
@@ -130,7 +157,6 @@ ${PYSITELIB}/statsmodels/datasets/cpunish/cpunish.csv
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${PYSITELIB}/statsmodels/datasets/cpunish/data.pyc
${PYSITELIB}/statsmodels/datasets/cpunish/data.pyo
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${PYSITELIB}/statsmodels/datasets/elnino/__init__.py
${PYSITELIB}/statsmodels/datasets/elnino/__init__.pyc
${PYSITELIB}/statsmodels/datasets/elnino/__init__.pyo
@@ -138,7 +164,6 @@ ${PYSITELIB}/statsmodels/datasets/elnino/data.py
${PYSITELIB}/statsmodels/datasets/elnino/data.pyc
${PYSITELIB}/statsmodels/datasets/elnino/data.pyo
${PYSITELIB}/statsmodels/datasets/elnino/elnino.csv
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${PYSITELIB}/statsmodels/datasets/engel/__init__.py
${PYSITELIB}/statsmodels/datasets/engel/__init__.pyc
${PYSITELIB}/statsmodels/datasets/engel/__init__.pyo
@@ -153,7 +178,13 @@ ${PYSITELIB}/statsmodels/datasets/fair/data.py
${PYSITELIB}/statsmodels/datasets/fair/data.pyc
${PYSITELIB}/statsmodels/datasets/fair/data.pyo
${PYSITELIB}/statsmodels/datasets/fair/fair.csv
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${PYSITELIB}/statsmodels/datasets/grunfeld/__init__.py
${PYSITELIB}/statsmodels/datasets/grunfeld/__init__.pyc
${PYSITELIB}/statsmodels/datasets/grunfeld/__init__.pyo
@@ -161,7 +192,6 @@ ${PYSITELIB}/statsmodels/datasets/grunfeld/data.py
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${PYSITELIB}/statsmodels/datasets/grunfeld/data.pyo
${PYSITELIB}/statsmodels/datasets/grunfeld/grunfeld.csv
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${PYSITELIB}/statsmodels/datasets/heart/__init__.py
${PYSITELIB}/statsmodels/datasets/heart/__init__.pyc
${PYSITELIB}/statsmodels/datasets/heart/__init__.pyo
@@ -169,8 +199,6 @@ ${PYSITELIB}/statsmodels/datasets/heart/data.py
${PYSITELIB}/statsmodels/datasets/heart/data.pyc
${PYSITELIB}/statsmodels/datasets/heart/data.pyo
${PYSITELIB}/statsmodels/datasets/heart/heart.csv
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${PYSITELIB}/statsmodels/datasets/longley/__init__.py
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@@ -186,8 +214,13 @@ ${PYSITELIB}/statsmodels/datasets/macrodata/data.pyc
${PYSITELIB}/statsmodels/datasets/macrodata/data.pyo
${PYSITELIB}/statsmodels/datasets/macrodata/macrodata.csv
${PYSITELIB}/statsmodels/datasets/macrodata/macrodata.dta
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${PYSITELIB}/statsmodels/datasets/nile/__init__.py
${PYSITELIB}/statsmodels/datasets/nile/__init__.pyc
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@@ -202,9 +235,6 @@ ${PYSITELIB}/statsmodels/datasets/randhie/data.py
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${PYSITELIB}/statsmodels/datasets/randhie/data.pyo
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${PYSITELIB}/statsmodels/datasets/scotland/__init__.pyc
${PYSITELIB}/statsmodels/datasets/scotland/__init__.pyo
@@ -212,18 +242,6 @@ ${PYSITELIB}/statsmodels/datasets/scotland/data.py
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${PYSITELIB}/statsmodels/datasets/scotland/data.pyo
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@@ -231,7 +249,6 @@ ${PYSITELIB}/statsmodels/datasets/spector/data.py
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${PYSITELIB}/statsmodels/datasets/spector/data.pyo
${PYSITELIB}/statsmodels/datasets/spector/spector.csv
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${PYSITELIB}/statsmodels/datasets/stackloss/__init__.pyc
${PYSITELIB}/statsmodels/datasets/stackloss/__init__.pyo
@@ -245,10 +262,6 @@ ${PYSITELIB}/statsmodels/datasets/star98/__init__.pyo
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${PYSITELIB}/statsmodels/datasets/statecrime/__init__.py
${PYSITELIB}/statsmodels/datasets/statecrime/__init__.pyc
@@ -264,16 +277,12 @@ ${PYSITELIB}/statsmodels/datasets/strikes/data.py
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${PYSITELIB}/statsmodels/datasets/strikes/data.pyo
${PYSITELIB}/statsmodels/datasets/strikes/strikes.csv
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${PYSITELIB}/statsmodels/datasets/sunspots/__init__.pyc
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${PYSITELIB}/statsmodels/datasets/sunspots/sunspots.csv
${PYSITELIB}/statsmodels/datasets/template_data.py
${PYSITELIB}/statsmodels/datasets/template_data.pyc
@@ -284,6 +293,9 @@ ${PYSITELIB}/statsmodels/datasets/tests/__init__.pyo
${PYSITELIB}/statsmodels/datasets/tests/raw.github.com,vincentarelbundock,Rdatasets,master,csv,car,Duncan.csv.zip
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${PYSITELIB}/statsmodels/datasets/tests/raw.github.com,vincentarelbundock,Rdatasets,master,doc,car,rst,Duncan.rst.zip
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@@ -310,17 +322,37 @@ ${PYSITELIB}/statsmodels/discrete/tests/results/mnlogit_resid.csv
${PYSITELIB}/statsmodels/discrete/tests/results/nbinom_resids.csv
${PYSITELIB}/statsmodels/discrete/tests/results/phat_mnlogit.csv
${PYSITELIB}/statsmodels/discrete/tests/results/poisson_resid.csv
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${PYSITELIB}/statsmodels/discrete/tests/results/yhat_mnlogit.csv
${PYSITELIB}/statsmodels/discrete/tests/results/yhat_poisson.csv
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${PYSITELIB}/statsmodels/distributions/__init__.py
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@@ -333,9 +365,52 @@ ${PYSITELIB}/statsmodels/distributions/tests/__init__.pyo
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+${PYSITELIB}/statsmodels/duration/tests/results/survival_data_50_1.csv
+${PYSITELIB}/statsmodels/duration/tests/results/survival_data_50_2.csv
+${PYSITELIB}/statsmodels/duration/tests/survival_enet_r_results.py
+${PYSITELIB}/statsmodels/duration/tests/survival_enet_r_results.pyc
+${PYSITELIB}/statsmodels/duration/tests/survival_enet_r_results.pyo
+${PYSITELIB}/statsmodels/duration/tests/survival_r_results.py
+${PYSITELIB}/statsmodels/duration/tests/survival_r_results.pyc
+${PYSITELIB}/statsmodels/duration/tests/survival_r_results.pyo
+${PYSITELIB}/statsmodels/duration/tests/test_phreg.py
+${PYSITELIB}/statsmodels/duration/tests/test_phreg.pyc
+${PYSITELIB}/statsmodels/duration/tests/test_phreg.pyo
+${PYSITELIB}/statsmodels/duration/tests/test_survfunc.py
+${PYSITELIB}/statsmodels/duration/tests/test_survfunc.pyc
+${PYSITELIB}/statsmodels/duration/tests/test_survfunc.pyo
${PYSITELIB}/statsmodels/emplike/__init__.py
${PYSITELIB}/statsmodels/emplike/__init__.pyc
${PYSITELIB}/statsmodels/emplike/__init__.pyo
@@ -354,7 +429,6 @@ ${PYSITELIB}/statsmodels/emplike/elanova.pyo
${PYSITELIB}/statsmodels/emplike/elregress.py
${PYSITELIB}/statsmodels/emplike/elregress.pyc
${PYSITELIB}/statsmodels/emplike/elregress.pyo
-${PYSITELIB}/statsmodels/emplike/heartdata.csv
${PYSITELIB}/statsmodels/emplike/koul_and_mc.py
${PYSITELIB}/statsmodels/emplike/koul_and_mc.pyc
${PYSITELIB}/statsmodels/emplike/koul_and_mc.pyo
@@ -385,213 +459,6 @@ ${PYSITELIB}/statsmodels/emplike/tests/test_origin.pyo
${PYSITELIB}/statsmodels/emplike/tests/test_regression.py
${PYSITELIB}/statsmodels/emplike/tests/test_regression.pyc
${PYSITELIB}/statsmodels/emplike/tests/test_regression.pyo
-${PYSITELIB}/statsmodels/examples/es_misc_poisson2.py
-${PYSITELIB}/statsmodels/examples/es_misc_poisson2.pyc
-${PYSITELIB}/statsmodels/examples/es_misc_poisson2.pyo
-${PYSITELIB}/statsmodels/examples/ex_arch_canada.py
-${PYSITELIB}/statsmodels/examples/ex_arch_canada.pyc
-${PYSITELIB}/statsmodels/examples/ex_arch_canada.pyo
-${PYSITELIB}/statsmodels/examples/ex_emplike_1.py
-${PYSITELIB}/statsmodels/examples/ex_emplike_1.pyc
-${PYSITELIB}/statsmodels/examples/ex_emplike_1.pyo
-${PYSITELIB}/statsmodels/examples/ex_emplike_2.py
-${PYSITELIB}/statsmodels/examples/ex_emplike_2.pyc
-${PYSITELIB}/statsmodels/examples/ex_emplike_2.pyo
-${PYSITELIB}/statsmodels/examples/ex_emplike_3.py
-${PYSITELIB}/statsmodels/examples/ex_emplike_3.pyc
-${PYSITELIB}/statsmodels/examples/ex_emplike_3.pyo
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het.py
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het.pyc
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het.pyo
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het_0.py
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het_0.pyc
-${PYSITELIB}/statsmodels/examples/ex_feasible_gls_het_0.pyo
-${PYSITELIB}/statsmodels/examples/ex_generic_mle.py
-${PYSITELIB}/statsmodels/examples/ex_generic_mle.pyc
-${PYSITELIB}/statsmodels/examples/ex_generic_mle.pyo
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_t.py
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_t.pyc
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_t.pyo
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_tdist.py
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_tdist.pyc
-${PYSITELIB}/statsmodels/examples/ex_generic_mle_tdist.pyo
-${PYSITELIB}/statsmodels/examples/ex_grangercausality.py
-${PYSITELIB}/statsmodels/examples/ex_grangercausality.pyc
-${PYSITELIB}/statsmodels/examples/ex_grangercausality.pyo
-${PYSITELIB}/statsmodels/examples/ex_inter_rater.py
-${PYSITELIB}/statsmodels/examples/ex_inter_rater.pyc
-${PYSITELIB}/statsmodels/examples/ex_inter_rater.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression2.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression2.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression2.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression3.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression3.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression3.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_censored2.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_censored2.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_censored2.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_dgp.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_dgp.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_dgp.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_sigtest.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_sigtest.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_regression_sigtest.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_semilinear_dgp.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_semilinear_dgp.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_semilinear_dgp.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_singleindex_dgp.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_singleindex_dgp.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_singleindex_dgp.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_test_functional.py
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-${PYSITELIB}/statsmodels/examples/ex_kernel_test_functional.pyo
-${PYSITELIB}/statsmodels/examples/ex_kernel_test_functional_li_wang.py
-${PYSITELIB}/statsmodels/examples/ex_kernel_test_functional_li_wang.pyc
-${PYSITELIB}/statsmodels/examples/ex_kernel_test_functional_li_wang.pyo
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-${PYSITELIB}/statsmodels/examples/ex_lowess.pyc
-${PYSITELIB}/statsmodels/examples/ex_lowess.pyo
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-${PYSITELIB}/statsmodels/examples/ex_misc_tmodel.pyo
-${PYSITELIB}/statsmodels/examples/ex_multivar_kde.py
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-${PYSITELIB}/statsmodels/examples/ex_proportion.pyo
-${PYSITELIB}/statsmodels/examples/ex_regressionplots.py
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-${PYSITELIB}/statsmodels/examples/ex_rootfinding.py
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-${PYSITELIB}/statsmodels/examples/ex_sandwich2.py
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-${PYSITELIB}/statsmodels/examples/ex_scatter_ellipse.pyo
-${PYSITELIB}/statsmodels/examples/ex_shrink_pickle.py
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-${PYSITELIB}/statsmodels/examples/ex_univar_kde.pyo
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-${PYSITELIB}/statsmodels/examples/example_discrete_mnl.pyc
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-${PYSITELIB}/statsmodels/examples/l1_demo/demo.pyc
-${PYSITELIB}/statsmodels/examples/l1_demo/demo.pyo
-${PYSITELIB}/statsmodels/examples/l1_demo/short_demo.py
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-${PYSITELIB}/statsmodels/examples/l1_demo/sklearn_compare.py
-${PYSITELIB}/statsmodels/examples/l1_demo/sklearn_compare.pyc
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-${PYSITELIB}/statsmodels/examples/try_gof_chisquare.py
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-${PYSITELIB}/statsmodels/examples/try_power.pyo
-${PYSITELIB}/statsmodels/examples/try_power2.py
-${PYSITELIB}/statsmodels/examples/try_power2.pyc
-${PYSITELIB}/statsmodels/examples/try_power2.pyo
-${PYSITELIB}/statsmodels/examples/try_tukey_hsd.py
-${PYSITELIB}/statsmodels/examples/try_tukey_hsd.pyc
-${PYSITELIB}/statsmodels/examples/try_tukey_hsd.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ar1cholesky.py
-${PYSITELIB}/statsmodels/examples/tsa/ar1cholesky.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ar1cholesky.pyo
-${PYSITELIB}/statsmodels/examples/tsa/arma_plots.py
-${PYSITELIB}/statsmodels/examples/tsa/arma_plots.pyc
-${PYSITELIB}/statsmodels/examples/tsa/arma_plots.pyo
-${PYSITELIB}/statsmodels/examples/tsa/compare_arma.py
-${PYSITELIB}/statsmodels/examples/tsa/compare_arma.pyc
-${PYSITELIB}/statsmodels/examples/tsa/compare_arma.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma.py
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma_all.py
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma_all.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ex_arma_all.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ex_coint.py
-${PYSITELIB}/statsmodels/examples/tsa/ex_coint.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ex_coint.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ex_var.py
-${PYSITELIB}/statsmodels/examples/tsa/ex_var.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ex_var.pyo
-${PYSITELIB}/statsmodels/examples/tsa/ex_var_reorder.py
-${PYSITELIB}/statsmodels/examples/tsa/ex_var_reorder.pyc
-${PYSITELIB}/statsmodels/examples/tsa/ex_var_reorder.pyo
-${PYSITELIB}/statsmodels/examples/tsa/lagpolynomial.py
-${PYSITELIB}/statsmodels/examples/tsa/lagpolynomial.pyc
-${PYSITELIB}/statsmodels/examples/tsa/lagpolynomial.pyo
-${PYSITELIB}/statsmodels/examples/tsa/try_ar.py
-${PYSITELIB}/statsmodels/examples/tsa/try_ar.pyc
-${PYSITELIB}/statsmodels/examples/tsa/try_ar.pyo
-${PYSITELIB}/statsmodels/examples/tut_ols_ancova.py
-${PYSITELIB}/statsmodels/examples/tut_ols_ancova.pyc
-${PYSITELIB}/statsmodels/examples/tut_ols_ancova.pyo
-${PYSITELIB}/statsmodels/examples/tut_ols_rlm_short.py
-${PYSITELIB}/statsmodels/examples/tut_ols_rlm_short.pyc
-${PYSITELIB}/statsmodels/examples/tut_ols_rlm_short.pyo
${PYSITELIB}/statsmodels/formula/__init__.py
${PYSITELIB}/statsmodels/formula/__init__.pyc
${PYSITELIB}/statsmodels/formula/__init__.pyo
@@ -610,6 +477,18 @@ ${PYSITELIB}/statsmodels/formula/tests/test_formula.pyo
${PYSITELIB}/statsmodels/genmod/__init__.py
${PYSITELIB}/statsmodels/genmod/__init__.pyc
${PYSITELIB}/statsmodels/genmod/__init__.pyo
+${PYSITELIB}/statsmodels/genmod/_prediction.py
+${PYSITELIB}/statsmodels/genmod/_prediction.pyc
+${PYSITELIB}/statsmodels/genmod/_prediction.pyo
+${PYSITELIB}/statsmodels/genmod/_tweedie_compound_poisson.py
+${PYSITELIB}/statsmodels/genmod/_tweedie_compound_poisson.pyc
+${PYSITELIB}/statsmodels/genmod/_tweedie_compound_poisson.pyo
+${PYSITELIB}/statsmodels/genmod/api.py
+${PYSITELIB}/statsmodels/genmod/api.pyc
+${PYSITELIB}/statsmodels/genmod/api.pyo
+${PYSITELIB}/statsmodels/genmod/cov_struct.py
+${PYSITELIB}/statsmodels/genmod/cov_struct.pyc
+${PYSITELIB}/statsmodels/genmod/cov_struct.pyo
${PYSITELIB}/statsmodels/genmod/families/__init__.py
${PYSITELIB}/statsmodels/genmod/families/__init__.pyc
${PYSITELIB}/statsmodels/genmod/families/__init__.pyo
@@ -619,18 +498,57 @@ ${PYSITELIB}/statsmodels/genmod/families/family.pyo
${PYSITELIB}/statsmodels/genmod/families/links.py
${PYSITELIB}/statsmodels/genmod/families/links.pyc
${PYSITELIB}/statsmodels/genmod/families/links.pyo
+${PYSITELIB}/statsmodels/genmod/families/tests/__init__.py
+${PYSITELIB}/statsmodels/genmod/families/tests/__init__.pyc
+${PYSITELIB}/statsmodels/genmod/families/tests/__init__.pyo
+${PYSITELIB}/statsmodels/genmod/families/tests/test_link.py
+${PYSITELIB}/statsmodels/genmod/families/tests/test_link.pyc
+${PYSITELIB}/statsmodels/genmod/families/tests/test_link.pyo
${PYSITELIB}/statsmodels/genmod/families/varfuncs.py
${PYSITELIB}/statsmodels/genmod/families/varfuncs.pyc
${PYSITELIB}/statsmodels/genmod/families/varfuncs.pyo
+${PYSITELIB}/statsmodels/genmod/generalized_estimating_equations.py
+${PYSITELIB}/statsmodels/genmod/generalized_estimating_equations.pyc
+${PYSITELIB}/statsmodels/genmod/generalized_estimating_equations.pyo
${PYSITELIB}/statsmodels/genmod/generalized_linear_model.py
${PYSITELIB}/statsmodels/genmod/generalized_linear_model.pyc
${PYSITELIB}/statsmodels/genmod/generalized_linear_model.pyo
${PYSITELIB}/statsmodels/genmod/tests/__init__.py
${PYSITELIB}/statsmodels/genmod/tests/__init__.pyc
${PYSITELIB}/statsmodels/genmod/tests/__init__.pyo
+${PYSITELIB}/statsmodels/genmod/tests/gee_categorical_simulation_check.py
+${PYSITELIB}/statsmodels/genmod/tests/gee_categorical_simulation_check.pyc
+${PYSITELIB}/statsmodels/genmod/tests/gee_categorical_simulation_check.pyo
+${PYSITELIB}/statsmodels/genmod/tests/gee_gaussian_simulation_check.py
+${PYSITELIB}/statsmodels/genmod/tests/gee_gaussian_simulation_check.pyc
+${PYSITELIB}/statsmodels/genmod/tests/gee_gaussian_simulation_check.pyo
+${PYSITELIB}/statsmodels/genmod/tests/gee_poisson_simulation_check.py
+${PYSITELIB}/statsmodels/genmod/tests/gee_poisson_simulation_check.pyc
+${PYSITELIB}/statsmodels/genmod/tests/gee_poisson_simulation_check.pyo
+${PYSITELIB}/statsmodels/genmod/tests/gee_simulation_check.py
+${PYSITELIB}/statsmodels/genmod/tests/gee_simulation_check.pyc
+${PYSITELIB}/statsmodels/genmod/tests/gee_simulation_check.pyo
+${PYSITELIB}/statsmodels/genmod/tests/glmnet_r_results.py
+${PYSITELIB}/statsmodels/genmod/tests/glmnet_r_results.pyc
+${PYSITELIB}/statsmodels/genmod/tests/glmnet_r_results.pyo
${PYSITELIB}/statsmodels/genmod/tests/results/__init__.py
${PYSITELIB}/statsmodels/genmod/tests/results/__init__.pyc
${PYSITELIB}/statsmodels/genmod/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/genmod/tests/results/elastic_net_generate_tests.py
+${PYSITELIB}/statsmodels/genmod/tests/results/elastic_net_generate_tests.pyc
+${PYSITELIB}/statsmodels/genmod/tests/results/elastic_net_generate_tests.pyo
+${PYSITELIB}/statsmodels/genmod/tests/results/enet_binomial.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/enet_poisson.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/epil.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_generate_tests.py
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_generate_tests.pyc
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_generate_tests.pyo
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_linear_1.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_logistic_1.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_nested_linear_1.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_nominal_1.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_ordinal_1.csv
+${PYSITELIB}/statsmodels/genmod/tests/results/gee_poisson_1.csv
${PYSITELIB}/statsmodels/genmod/tests/results/glm_test_resids.py
${PYSITELIB}/statsmodels/genmod/tests/results/glm_test_resids.pyc
${PYSITELIB}/statsmodels/genmod/tests/results/glm_test_resids.pyo
@@ -641,15 +559,27 @@ ${PYSITELIB}/statsmodels/genmod/tests/results/medparlogresids.csv
${PYSITELIB}/statsmodels/genmod/tests/results/results_glm.py
${PYSITELIB}/statsmodels/genmod/tests/results/results_glm.pyc
${PYSITELIB}/statsmodels/genmod/tests/results/results_glm.pyo
+${PYSITELIB}/statsmodels/genmod/tests/results/results_glm_poisson_weights.py
+${PYSITELIB}/statsmodels/genmod/tests/results/results_glm_poisson_weights.pyc
+${PYSITELIB}/statsmodels/genmod/tests/results/results_glm_poisson_weights.pyo
${PYSITELIB}/statsmodels/genmod/tests/results/stata_cancer_glm.csv
${PYSITELIB}/statsmodels/genmod/tests/results/stata_lbw_glm.csv
${PYSITELIB}/statsmodels/genmod/tests/results/stata_medpar1_glm.csv
+${PYSITELIB}/statsmodels/genmod/tests/test_gee.py
+${PYSITELIB}/statsmodels/genmod/tests/test_gee.pyc
+${PYSITELIB}/statsmodels/genmod/tests/test_gee.pyo
${PYSITELIB}/statsmodels/genmod/tests/test_glm.py
${PYSITELIB}/statsmodels/genmod/tests/test_glm.pyc
${PYSITELIB}/statsmodels/genmod/tests/test_glm.pyo
+${PYSITELIB}/statsmodels/genmod/tests/test_glm_weights.py
+${PYSITELIB}/statsmodels/genmod/tests/test_glm_weights.pyc
+${PYSITELIB}/statsmodels/genmod/tests/test_glm_weights.pyo
${PYSITELIB}/statsmodels/graphics/__init__.py
${PYSITELIB}/statsmodels/graphics/__init__.pyc
${PYSITELIB}/statsmodels/graphics/__init__.pyo
+${PYSITELIB}/statsmodels/graphics/_regressionplots_doc.py
+${PYSITELIB}/statsmodels/graphics/_regressionplots_doc.pyc
+${PYSITELIB}/statsmodels/graphics/_regressionplots_doc.pyo
${PYSITELIB}/statsmodels/graphics/api.py
${PYSITELIB}/statsmodels/graphics/api.pyc
${PYSITELIB}/statsmodels/graphics/api.pyo
@@ -659,6 +589,9 @@ ${PYSITELIB}/statsmodels/graphics/boxplots.pyo
${PYSITELIB}/statsmodels/graphics/correlation.py
${PYSITELIB}/statsmodels/graphics/correlation.pyc
${PYSITELIB}/statsmodels/graphics/correlation.pyo
+${PYSITELIB}/statsmodels/graphics/dotplots.py
+${PYSITELIB}/statsmodels/graphics/dotplots.pyc
+${PYSITELIB}/statsmodels/graphics/dotplots.pyo
${PYSITELIB}/statsmodels/graphics/factorplots.py
${PYSITELIB}/statsmodels/graphics/factorplots.pyc
${PYSITELIB}/statsmodels/graphics/factorplots.pyo
@@ -689,6 +622,9 @@ ${PYSITELIB}/statsmodels/graphics/tests/test_boxplots.pyo
${PYSITELIB}/statsmodels/graphics/tests/test_correlation.py
${PYSITELIB}/statsmodels/graphics/tests/test_correlation.pyc
${PYSITELIB}/statsmodels/graphics/tests/test_correlation.pyo
+${PYSITELIB}/statsmodels/graphics/tests/test_dotplot.py
+${PYSITELIB}/statsmodels/graphics/tests/test_dotplot.pyc
+${PYSITELIB}/statsmodels/graphics/tests/test_dotplot.pyo
${PYSITELIB}/statsmodels/graphics/tests/test_factorplots.py
${PYSITELIB}/statsmodels/graphics/tests/test_factorplots.pyc
${PYSITELIB}/statsmodels/graphics/tests/test_factorplots.pyo
@@ -716,6 +652,24 @@ ${PYSITELIB}/statsmodels/graphics/tukeyplot.pyo
${PYSITELIB}/statsmodels/graphics/utils.py
${PYSITELIB}/statsmodels/graphics/utils.pyc
${PYSITELIB}/statsmodels/graphics/utils.pyo
+${PYSITELIB}/statsmodels/imputation/__init__.py
+${PYSITELIB}/statsmodels/imputation/__init__.pyc
+${PYSITELIB}/statsmodels/imputation/__init__.pyo
+${PYSITELIB}/statsmodels/imputation/mice.py
+${PYSITELIB}/statsmodels/imputation/mice.pyc
+${PYSITELIB}/statsmodels/imputation/mice.pyo
+${PYSITELIB}/statsmodels/imputation/ros.py
+${PYSITELIB}/statsmodels/imputation/ros.pyc
+${PYSITELIB}/statsmodels/imputation/ros.pyo
+${PYSITELIB}/statsmodels/imputation/tests/__init__.py
+${PYSITELIB}/statsmodels/imputation/tests/__init__.pyc
+${PYSITELIB}/statsmodels/imputation/tests/__init__.pyo
+${PYSITELIB}/statsmodels/imputation/tests/test_mice.py
+${PYSITELIB}/statsmodels/imputation/tests/test_mice.pyc
+${PYSITELIB}/statsmodels/imputation/tests/test_mice.pyo
+${PYSITELIB}/statsmodels/imputation/tests/test_ros.py
+${PYSITELIB}/statsmodels/imputation/tests/test_ros.pyc
+${PYSITELIB}/statsmodels/imputation/tests/test_ros.pyo
${PYSITELIB}/statsmodels/info.py
${PYSITELIB}/statsmodels/info.pyc
${PYSITELIB}/statsmodels/info.pyo
@@ -731,7 +685,9 @@ ${PYSITELIB}/statsmodels/iolib/api.pyo
${PYSITELIB}/statsmodels/iolib/foreign.py
${PYSITELIB}/statsmodels/iolib/foreign.pyc
${PYSITELIB}/statsmodels/iolib/foreign.pyo
-${PYSITELIB}/statsmodels/iolib/notes_table_update.txt
+${PYSITELIB}/statsmodels/iolib/openfile.py
+${PYSITELIB}/statsmodels/iolib/openfile.pyc
+${PYSITELIB}/statsmodels/iolib/openfile.pyo
${PYSITELIB}/statsmodels/iolib/smpickle.py
${PYSITELIB}/statsmodels/iolib/smpickle.pyc
${PYSITELIB}/statsmodels/iolib/smpickle.pyo
@@ -757,13 +713,10 @@ ${PYSITELIB}/statsmodels/iolib/tests/results/__init__.py
${PYSITELIB}/statsmodels/iolib/tests/results/__init__.pyc
${PYSITELIB}/statsmodels/iolib/tests/results/__init__.pyo
${PYSITELIB}/statsmodels/iolib/tests/results/data_missing.dta
-${PYSITELIB}/statsmodels/iolib/tests/results/macrodata.npy_
${PYSITELIB}/statsmodels/iolib/tests/results/macrodata.py
${PYSITELIB}/statsmodels/iolib/tests/results/macrodata.pyc
${PYSITELIB}/statsmodels/iolib/tests/results/macrodata.pyo
${PYSITELIB}/statsmodels/iolib/tests/results/time_series_examples.dta
-${PYSITELIB}/statsmodels/iolib/tests/stata_dates.csv
-${PYSITELIB}/statsmodels/iolib/tests/test_data.csv
${PYSITELIB}/statsmodels/iolib/tests/test_foreign.py
${PYSITELIB}/statsmodels/iolib/tests/test_foreign.pyc
${PYSITELIB}/statsmodels/iolib/tests/test_foreign.pyo
@@ -818,14 +771,30 @@ ${PYSITELIB}/statsmodels/miscmodels/tmodel.pyo
${PYSITELIB}/statsmodels/miscmodels/try_mlecov.py
${PYSITELIB}/statsmodels/miscmodels/try_mlecov.pyc
${PYSITELIB}/statsmodels/miscmodels/try_mlecov.pyo
+${PYSITELIB}/statsmodels/multivariate/__init__.py
+${PYSITELIB}/statsmodels/multivariate/__init__.pyc
+${PYSITELIB}/statsmodels/multivariate/__init__.pyo
+${PYSITELIB}/statsmodels/multivariate/pca.py
+${PYSITELIB}/statsmodels/multivariate/pca.pyc
+${PYSITELIB}/statsmodels/multivariate/pca.pyo
+${PYSITELIB}/statsmodels/multivariate/tests/__init__.py
+${PYSITELIB}/statsmodels/multivariate/tests/__init__.pyc
+${PYSITELIB}/statsmodels/multivariate/tests/__init__.pyo
+${PYSITELIB}/statsmodels/multivariate/tests/results/__init__.py
+${PYSITELIB}/statsmodels/multivariate/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/multivariate/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/multivariate/tests/results/datamlw.py
+${PYSITELIB}/statsmodels/multivariate/tests/results/datamlw.pyc
+${PYSITELIB}/statsmodels/multivariate/tests/results/datamlw.pyo
+${PYSITELIB}/statsmodels/multivariate/tests/test_pca.py
+${PYSITELIB}/statsmodels/multivariate/tests/test_pca.pyc
+${PYSITELIB}/statsmodels/multivariate/tests/test_pca.pyo
${PYSITELIB}/statsmodels/nonparametric/__init__.py
${PYSITELIB}/statsmodels/nonparametric/__init__.pyc
${PYSITELIB}/statsmodels/nonparametric/__init__.pyo
${PYSITELIB}/statsmodels/nonparametric/_kernel_base.py
${PYSITELIB}/statsmodels/nonparametric/_kernel_base.pyc
${PYSITELIB}/statsmodels/nonparametric/_kernel_base.pyo
-${PYSITELIB}/statsmodels/nonparametric/_smoothers_lowess.c
-${PYSITELIB}/statsmodels/nonparametric/_smoothers_lowess.pyx
${PYSITELIB}/statsmodels/nonparametric/_smoothers_lowess.so
${PYSITELIB}/statsmodels/nonparametric/api.py
${PYSITELIB}/statsmodels/nonparametric/api.pyc
@@ -848,8 +817,6 @@ ${PYSITELIB}/statsmodels/nonparametric/kernel_regression.pyo
${PYSITELIB}/statsmodels/nonparametric/kernels.py
${PYSITELIB}/statsmodels/nonparametric/kernels.pyc
${PYSITELIB}/statsmodels/nonparametric/kernels.pyo
-${PYSITELIB}/statsmodels/nonparametric/linbin.c
-${PYSITELIB}/statsmodels/nonparametric/linbin.pyx
${PYSITELIB}/statsmodels/nonparametric/linbin.so
${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess.py
${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess.pyc
@@ -857,20 +824,25 @@ ${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess.pyo
${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess_old.py
${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess_old.pyc
${PYSITELIB}/statsmodels/nonparametric/smoothers_lowess_old.pyo
-${PYSITELIB}/statsmodels/nonparametric/tests/Xi_test_data.csv
${PYSITELIB}/statsmodels/nonparametric/tests/__init__.py
${PYSITELIB}/statsmodels/nonparametric/tests/__init__.pyc
${PYSITELIB}/statsmodels/nonparametric/tests/__init__.pyo
${PYSITELIB}/statsmodels/nonparametric/tests/results/__init__.py
${PYSITELIB}/statsmodels/nonparametric/tests/results/__init__.pyc
${PYSITELIB}/statsmodels/nonparametric/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kcde.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kde.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kde_fft.csv
+${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kde_weights.csv
+${PYSITELIB}/statsmodels/nonparametric/tests/results/results_kernel_regression.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/test_lowess_delta.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/test_lowess_frac.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/test_lowess_iter.csv
${PYSITELIB}/statsmodels/nonparametric/tests/results/test_lowess_simple.csv
+${PYSITELIB}/statsmodels/nonparametric/tests/test_bandwidths.py
+${PYSITELIB}/statsmodels/nonparametric/tests/test_bandwidths.pyc
+${PYSITELIB}/statsmodels/nonparametric/tests/test_bandwidths.pyo
${PYSITELIB}/statsmodels/nonparametric/tests/test_kde.py
${PYSITELIB}/statsmodels/nonparametric/tests/test_kde.pyc
${PYSITELIB}/statsmodels/nonparametric/tests/test_kde.pyo
@@ -880,34 +852,80 @@ ${PYSITELIB}/statsmodels/nonparametric/tests/test_kernel_density.pyo
${PYSITELIB}/statsmodels/nonparametric/tests/test_kernel_regression.py
${PYSITELIB}/statsmodels/nonparametric/tests/test_kernel_regression.pyc
${PYSITELIB}/statsmodels/nonparametric/tests/test_kernel_regression.pyo
+${PYSITELIB}/statsmodels/nonparametric/tests/test_kernels.py
+${PYSITELIB}/statsmodels/nonparametric/tests/test_kernels.pyc
+${PYSITELIB}/statsmodels/nonparametric/tests/test_kernels.pyo
${PYSITELIB}/statsmodels/nonparametric/tests/test_lowess.py
${PYSITELIB}/statsmodels/nonparametric/tests/test_lowess.pyc
${PYSITELIB}/statsmodels/nonparametric/tests/test_lowess.pyo
${PYSITELIB}/statsmodels/regression/__init__.py
${PYSITELIB}/statsmodels/regression/__init__.pyc
${PYSITELIB}/statsmodels/regression/__init__.pyo
+${PYSITELIB}/statsmodels/regression/_prediction.py
+${PYSITELIB}/statsmodels/regression/_prediction.pyc
+${PYSITELIB}/statsmodels/regression/_prediction.pyo
${PYSITELIB}/statsmodels/regression/feasible_gls.py
${PYSITELIB}/statsmodels/regression/feasible_gls.pyc
${PYSITELIB}/statsmodels/regression/feasible_gls.pyo
${PYSITELIB}/statsmodels/regression/linear_model.py
${PYSITELIB}/statsmodels/regression/linear_model.pyc
${PYSITELIB}/statsmodels/regression/linear_model.pyo
+${PYSITELIB}/statsmodels/regression/mixed_linear_model.py
+${PYSITELIB}/statsmodels/regression/mixed_linear_model.pyc
+${PYSITELIB}/statsmodels/regression/mixed_linear_model.pyo
${PYSITELIB}/statsmodels/regression/quantile_regression.py
${PYSITELIB}/statsmodels/regression/quantile_regression.pyc
${PYSITELIB}/statsmodels/regression/quantile_regression.pyo
+${PYSITELIB}/statsmodels/regression/recursive_ls.py
+${PYSITELIB}/statsmodels/regression/recursive_ls.pyc
+${PYSITELIB}/statsmodels/regression/recursive_ls.pyo
${PYSITELIB}/statsmodels/regression/tests/__init__.py
${PYSITELIB}/statsmodels/regression/tests/__init__.pyc
${PYSITELIB}/statsmodels/regression/tests/__init__.pyo
+${PYSITELIB}/statsmodels/regression/tests/generate_lasso.py
+${PYSITELIB}/statsmodels/regression/tests/generate_lasso.pyc
+${PYSITELIB}/statsmodels/regression/tests/generate_lasso.pyo
+${PYSITELIB}/statsmodels/regression/tests/generate_lme.py
+${PYSITELIB}/statsmodels/regression/tests/generate_lme.pyc
+${PYSITELIB}/statsmodels/regression/tests/generate_lme.pyo
+${PYSITELIB}/statsmodels/regression/tests/glmnet_r_results.py
+${PYSITELIB}/statsmodels/regression/tests/glmnet_r_results.pyc
+${PYSITELIB}/statsmodels/regression/tests/glmnet_r_results.pyo
+${PYSITELIB}/statsmodels/regression/tests/lme_r_results.py
+${PYSITELIB}/statsmodels/regression/tests/lme_r_results.pyc
+${PYSITELIB}/statsmodels/regression/tests/lme_r_results.pyo
${PYSITELIB}/statsmodels/regression/tests/results/__init__.py
${PYSITELIB}/statsmodels/regression/tests/results/__init__.pyc
${PYSITELIB}/statsmodels/regression/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/regression/tests/results/lasso_data.csv
${PYSITELIB}/statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt
+${PYSITELIB}/statsmodels/regression/tests/results/lme00.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme01.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme02.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme03.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme04.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme05.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme06.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme07.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme08.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme09.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme10.csv
+${PYSITELIB}/statsmodels/regression/tests/results/lme11.csv
${PYSITELIB}/statsmodels/regression/tests/results/macro_gr_corc_stata.py
${PYSITELIB}/statsmodels/regression/tests/results/macro_gr_corc_stata.pyc
${PYSITELIB}/statsmodels/regression/tests/results/macro_gr_corc_stata.pyo
+${PYSITELIB}/statsmodels/regression/tests/results/pastes.csv
+${PYSITELIB}/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py
+${PYSITELIB}/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.pyc
+${PYSITELIB}/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.pyo
+${PYSITELIB}/statsmodels/regression/tests/results/results_macro_ols_robust.py
+${PYSITELIB}/statsmodels/regression/tests/results/results_macro_ols_robust.pyc
+${PYSITELIB}/statsmodels/regression/tests/results/results_macro_ols_robust.pyo
${PYSITELIB}/statsmodels/regression/tests/results/results_regression.py
${PYSITELIB}/statsmodels/regression/tests/results/results_regression.pyc
${PYSITELIB}/statsmodels/regression/tests/results/results_regression.pyo
+${PYSITELIB}/statsmodels/regression/tests/results/results_rls_R.csv
+${PYSITELIB}/statsmodels/regression/tests/results/results_rls_stata.csv
${PYSITELIB}/statsmodels/regression/tests/results_quantile_regression.py
${PYSITELIB}/statsmodels/regression/tests/results_quantile_regression.pyc
${PYSITELIB}/statsmodels/regression/tests/results_quantile_regression.pyo
@@ -920,12 +938,24 @@ ${PYSITELIB}/statsmodels/regression/tests/test_glsar_gretl.pyo
${PYSITELIB}/statsmodels/regression/tests/test_glsar_stata.py
${PYSITELIB}/statsmodels/regression/tests/test_glsar_stata.pyc
${PYSITELIB}/statsmodels/regression/tests/test_glsar_stata.pyo
+${PYSITELIB}/statsmodels/regression/tests/test_lme.py
+${PYSITELIB}/statsmodels/regression/tests/test_lme.pyc
+${PYSITELIB}/statsmodels/regression/tests/test_lme.pyo
${PYSITELIB}/statsmodels/regression/tests/test_quantile_regression.py
${PYSITELIB}/statsmodels/regression/tests/test_quantile_regression.pyc
${PYSITELIB}/statsmodels/regression/tests/test_quantile_regression.pyo
+${PYSITELIB}/statsmodels/regression/tests/test_recursive_ls.py
+${PYSITELIB}/statsmodels/regression/tests/test_recursive_ls.pyc
+${PYSITELIB}/statsmodels/regression/tests/test_recursive_ls.pyo
${PYSITELIB}/statsmodels/regression/tests/test_regression.py
${PYSITELIB}/statsmodels/regression/tests/test_regression.pyc
${PYSITELIB}/statsmodels/regression/tests/test_regression.pyo
+${PYSITELIB}/statsmodels/regression/tests/test_robustcov.py
+${PYSITELIB}/statsmodels/regression/tests/test_robustcov.pyc
+${PYSITELIB}/statsmodels/regression/tests/test_robustcov.pyo
+${PYSITELIB}/statsmodels/regression/tests/tests_predict.py
+${PYSITELIB}/statsmodels/regression/tests/tests_predict.pyc
+${PYSITELIB}/statsmodels/regression/tests/tests_predict.pyo
${PYSITELIB}/statsmodels/resampling/__init__.py
${PYSITELIB}/statsmodels/resampling/__init__.pyc
${PYSITELIB}/statsmodels/resampling/__init__.pyo
@@ -1001,6 +1031,9 @@ ${PYSITELIB}/statsmodels/sandbox/distributions/estimators.pyo
${PYSITELIB}/statsmodels/sandbox/distributions/examples/__init__.py
${PYSITELIB}/statsmodels/sandbox/distributions/examples/__init__.pyc
${PYSITELIB}/statsmodels/sandbox/distributions/examples/__init__.pyo
+${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_extras.py
+${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_extras.pyc
+${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_extras.pyo
${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_fitfr.py
${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_fitfr.pyc
${PYSITELIB}/statsmodels/sandbox/distributions/examples/ex_fitfr.pyo
@@ -1061,6 +1094,9 @@ ${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_extras.pyo
${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_multivariate.py
${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_multivariate.pyc
${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_multivariate.pyo
+${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_norm_expan.py
+${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_norm_expan.pyc
+${PYSITELIB}/statsmodels/sandbox/distributions/tests/test_norm_expan.pyo
${PYSITELIB}/statsmodels/sandbox/distributions/tests/testtransf.py
${PYSITELIB}/statsmodels/sandbox/distributions/tests/testtransf.pyc
${PYSITELIB}/statsmodels/sandbox/distributions/tests/testtransf.pyo
@@ -1076,98 +1112,6 @@ ${PYSITELIB}/statsmodels/sandbox/distributions/try_max.pyo
${PYSITELIB}/statsmodels/sandbox/distributions/try_pot.py
${PYSITELIB}/statsmodels/sandbox/distributions/try_pot.pyc
${PYSITELIB}/statsmodels/sandbox/distributions/try_pot.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/bayesprior.py
-${PYSITELIB}/statsmodels/sandbox/examples/bayesprior.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/bayesprior.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/dji_table.csv
-${PYSITELIB}/statsmodels/sandbox/examples/ex_cusum.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_cusum.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_cusum.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula_factor.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula_factor.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_formula_factor.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_gam_results.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_gam_results.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_gam_results.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_kaplan_meier.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_kaplan_meier.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_kaplan_meier.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_0.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_0.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_0.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_re.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_re.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_re.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_onewaygls.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_onewaygls.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_onewaygls.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/ex_random_panel.py
-${PYSITELIB}/statsmodels/sandbox/examples/ex_random_panel.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/ex_random_panel.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_crossval.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_crossval.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_crossval.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam_0.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam_0.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_gam_0.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_garch.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_garch.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_garch.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_maxent.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_maxent.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_maxent.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_mle.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_mle.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_mle.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_nbin.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_nbin.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_nbin.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca_regression.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca_regression.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_pca_regression.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/example_sysreg.py
-${PYSITELIB}/statsmodels/sandbox/examples/example_sysreg.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/example_sysreg.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/gspc_table.csv
-${PYSITELIB}/statsmodels/sandbox/examples/run_all.py
-${PYSITELIB}/statsmodels/sandbox/examples/run_all.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/run_all.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_0.py
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_0.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_0.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_1.py
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_1.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/findow_1.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/try_interchange.py
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/try_interchange.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/thirdparty/try_interchange.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/try_multiols.py
-${PYSITELIB}/statsmodels/sandbox/examples/try_multiols.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/try_multiols.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression.py
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression1.py
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression1.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/try_quantile_regression1.pyo
-${PYSITELIB}/statsmodels/sandbox/examples/try_smoothers.py
-${PYSITELIB}/statsmodels/sandbox/examples/try_smoothers.pyc
-${PYSITELIB}/statsmodels/sandbox/examples/try_smoothers.pyo
${PYSITELIB}/statsmodels/sandbox/formula.py
${PYSITELIB}/statsmodels/sandbox/formula.pyc
${PYSITELIB}/statsmodels/sandbox/formula.pyo
@@ -1186,7 +1130,6 @@ ${PYSITELIB}/statsmodels/sandbox/mcevaluate/__init__.pyo
${PYSITELIB}/statsmodels/sandbox/mcevaluate/arma.py
${PYSITELIB}/statsmodels/sandbox/mcevaluate/arma.pyc
${PYSITELIB}/statsmodels/sandbox/mcevaluate/arma.pyo
-${PYSITELIB}/statsmodels/sandbox/mcevaluate/mcresuts_arma1.txt
${PYSITELIB}/statsmodels/sandbox/mle.py
${PYSITELIB}/statsmodels/sandbox/mle.pyc
${PYSITELIB}/statsmodels/sandbox/mle.pyo
@@ -1271,6 +1214,9 @@ ${PYSITELIB}/statsmodels/sandbox/panel/tests/test_random_panel.pyo
${PYSITELIB}/statsmodels/sandbox/pca.py
${PYSITELIB}/statsmodels/sandbox/pca.pyc
${PYSITELIB}/statsmodels/sandbox/pca.pyo
+${PYSITELIB}/statsmodels/sandbox/predict_functional.py
+${PYSITELIB}/statsmodels/sandbox/predict_functional.pyc
+${PYSITELIB}/statsmodels/sandbox/predict_functional.pyo
${PYSITELIB}/statsmodels/sandbox/regression/__init__.py
${PYSITELIB}/statsmodels/sandbox/regression/__init__.pyc
${PYSITELIB}/statsmodels/sandbox/regression/__init__.pyo
@@ -1280,18 +1226,6 @@ ${PYSITELIB}/statsmodels/sandbox/regression/anova_nistcertified.pyo
${PYSITELIB}/statsmodels/sandbox/regression/ar_panel.py
${PYSITELIB}/statsmodels/sandbox/regression/ar_panel.pyc
${PYSITELIB}/statsmodels/sandbox/regression/ar_panel.pyo
-${PYSITELIB}/statsmodels/sandbox/regression/data/AtmWtAg.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/Longley.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SiRstv.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs01.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs02.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs03.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs04.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs05.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs06.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs07.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs08.dat
-${PYSITELIB}/statsmodels/sandbox/regression/data/SmLs09.dat
${PYSITELIB}/statsmodels/sandbox/regression/example_kernridge.py
${PYSITELIB}/statsmodels/sandbox/regression/example_kernridge.pyc
${PYSITELIB}/statsmodels/sandbox/regression/example_kernridge.pyo
@@ -1301,7 +1235,6 @@ ${PYSITELIB}/statsmodels/sandbox/regression/gmm.pyo
${PYSITELIB}/statsmodels/sandbox/regression/kernridgeregress_class.py
${PYSITELIB}/statsmodels/sandbox/regression/kernridgeregress_class.pyc
${PYSITELIB}/statsmodels/sandbox/regression/kernridgeregress_class.pyo
-${PYSITELIB}/statsmodels/sandbox/regression/notes_runmnl.txt
${PYSITELIB}/statsmodels/sandbox/regression/ols_anova_original.py
${PYSITELIB}/statsmodels/sandbox/regression/ols_anova_original.pyc
${PYSITELIB}/statsmodels/sandbox/regression/ols_anova_original.pyo
@@ -1320,6 +1253,29 @@ ${PYSITELIB}/statsmodels/sandbox/regression/runmnl.pyo
${PYSITELIB}/statsmodels/sandbox/regression/sympy_diff.py
${PYSITELIB}/statsmodels/sandbox/regression/sympy_diff.pyc
${PYSITELIB}/statsmodels/sandbox/regression/sympy_diff.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/__init__.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/__init__.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/__init__.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/griliches76.dta
+${PYSITELIB}/statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_poisson.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_poisson.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_gmm_poisson.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm.pyo
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm_poisson.py
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm_poisson.pyc
+${PYSITELIB}/statsmodels/sandbox/regression/tests/test_gmm_poisson.pyo
${PYSITELIB}/statsmodels/sandbox/regression/tools.py
${PYSITELIB}/statsmodels/sandbox/regression/tools.pyc
${PYSITELIB}/statsmodels/sandbox/regression/tools.pyo
@@ -1338,7 +1294,6 @@ ${PYSITELIB}/statsmodels/sandbox/regression/try_treewalker.pyo
${PYSITELIB}/statsmodels/sandbox/rls.py
${PYSITELIB}/statsmodels/sandbox/rls.pyc
${PYSITELIB}/statsmodels/sandbox/rls.pyo
-${PYSITELIB}/statsmodels/sandbox/rlsdata.txt
${PYSITELIB}/statsmodels/sandbox/stats/__init__.py
${PYSITELIB}/statsmodels/sandbox/stats/__init__.pyc
${PYSITELIB}/statsmodels/sandbox/stats/__init__.pyo
@@ -1354,7 +1309,6 @@ ${PYSITELIB}/statsmodels/sandbox/stats/ex_newtests.pyo
${PYSITELIB}/statsmodels/sandbox/stats/multicomp.py
${PYSITELIB}/statsmodels/sandbox/stats/multicomp.pyc
${PYSITELIB}/statsmodels/sandbox/stats/multicomp.pyo
-${PYSITELIB}/statsmodels/sandbox/stats/notes_fdr.txt
${PYSITELIB}/statsmodels/sandbox/stats/runs.py
${PYSITELIB}/statsmodels/sandbox/stats/runs.pyc
${PYSITELIB}/statsmodels/sandbox/stats/runs.pyo
@@ -1376,14 +1330,12 @@ ${PYSITELIB}/statsmodels/sandbox/survival2.pyo
${PYSITELIB}/statsmodels/sandbox/sysreg.py
${PYSITELIB}/statsmodels/sandbox/sysreg.pyc
${PYSITELIB}/statsmodels/sandbox/sysreg.pyo
-${PYSITELIB}/statsmodels/sandbox/tests/GreeneEx15_1.s
${PYSITELIB}/statsmodels/sandbox/tests/__init__.py
${PYSITELIB}/statsmodels/sandbox/tests/__init__.pyc
${PYSITELIB}/statsmodels/sandbox/tests/__init__.pyo
${PYSITELIB}/statsmodels/sandbox/tests/datamlw.py
${PYSITELIB}/statsmodels/sandbox/tests/datamlw.pyc
${PYSITELIB}/statsmodels/sandbox/tests/datamlw.pyo
-${PYSITELIB}/statsmodels/sandbox/tests/macrodata.s
${PYSITELIB}/statsmodels/sandbox/tests/maketests_mlabwrap.py
${PYSITELIB}/statsmodels/sandbox/tests/maketests_mlabwrap.pyc
${PYSITELIB}/statsmodels/sandbox/tests/maketests_mlabwrap.pyo
@@ -1393,8 +1345,6 @@ ${PYSITELIB}/statsmodels/sandbox/tests/model_results.pyo
${PYSITELIB}/statsmodels/sandbox/tests/savervs.py
${PYSITELIB}/statsmodels/sandbox/tests/savervs.pyc
${PYSITELIB}/statsmodels/sandbox/tests/savervs.pyo
-${PYSITELIB}/statsmodels/sandbox/tests/sysreg.s
-${PYSITELIB}/statsmodels/sandbox/tests/test_bspline.py.txt
${PYSITELIB}/statsmodels/sandbox/tests/test_formula.py
${PYSITELIB}/statsmodels/sandbox/tests/test_formula.pyc
${PYSITELIB}/statsmodels/sandbox/tests/test_formula.pyo
@@ -1404,7 +1354,9 @@ ${PYSITELIB}/statsmodels/sandbox/tests/test_gam.pyo
${PYSITELIB}/statsmodels/sandbox/tests/test_pca.py
${PYSITELIB}/statsmodels/sandbox/tests/test_pca.pyc
${PYSITELIB}/statsmodels/sandbox/tests/test_pca.pyo
-${PYSITELIB}/statsmodels/sandbox/tools/TODO.txt
+${PYSITELIB}/statsmodels/sandbox/tests/test_predict_functional.py
+${PYSITELIB}/statsmodels/sandbox/tests/test_predict_functional.pyc
+${PYSITELIB}/statsmodels/sandbox/tests/test_predict_functional.pyo
${PYSITELIB}/statsmodels/sandbox/tools/__init__.py
${PYSITELIB}/statsmodels/sandbox/tools/__init__.pyc
${PYSITELIB}/statsmodels/sandbox/tools/__init__.pyo
@@ -1432,18 +1384,6 @@ ${PYSITELIB}/statsmodels/sandbox/tsa/diffusion2.pyo
${PYSITELIB}/statsmodels/sandbox/tsa/example_arma.py
${PYSITELIB}/statsmodels/sandbox/tsa/example_arma.pyc
${PYSITELIB}/statsmodels/sandbox/tsa/example_arma.pyo
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_arma.py
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_arma.pyc
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_arma.pyo
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_garch.py
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_garch.pyc
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/ex_mle_garch.pyo
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/example_var.py
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/example_var.pyc
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/example_var.pyo
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/try_ld_nitime.py
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/try_ld_nitime.pyc
-${PYSITELIB}/statsmodels/sandbox/tsa/examples/try_ld_nitime.pyo
${PYSITELIB}/statsmodels/sandbox/tsa/fftarma.py
${PYSITELIB}/statsmodels/sandbox/tsa/fftarma.pyc
${PYSITELIB}/statsmodels/sandbox/tsa/fftarma.pyo
@@ -1453,7 +1393,6 @@ ${PYSITELIB}/statsmodels/sandbox/tsa/garch.pyo
${PYSITELIB}/statsmodels/sandbox/tsa/movstat.py
${PYSITELIB}/statsmodels/sandbox/tsa/movstat.pyc
${PYSITELIB}/statsmodels/sandbox/tsa/movstat.pyo
-${PYSITELIB}/statsmodels/sandbox/tsa/notes_organize.txt
${PYSITELIB}/statsmodels/sandbox/tsa/try_arma_more.py
${PYSITELIB}/statsmodels/sandbox/tsa/try_arma_more.pyc
${PYSITELIB}/statsmodels/sandbox/tsa/try_arma_more.pyo
@@ -1469,14 +1408,18 @@ ${PYSITELIB}/statsmodels/sandbox/tsa/varma.pyo
${PYSITELIB}/statsmodels/sandbox/utils_old.py
${PYSITELIB}/statsmodels/sandbox/utils_old.pyc
${PYSITELIB}/statsmodels/sandbox/utils_old.pyo
-${PYSITELIB}/statsmodels/src/bspline_ext.c
-${PYSITELIB}/statsmodels/src/bspline_impl.c
+${PYSITELIB}/statsmodels/src/__init__.py
+${PYSITELIB}/statsmodels/src/__init__.pyc
+${PYSITELIB}/statsmodels/src/__init__.pyo
${PYSITELIB}/statsmodels/stats/__init__.py
${PYSITELIB}/statsmodels/stats/__init__.pyc
${PYSITELIB}/statsmodels/stats/__init__.pyo
-${PYSITELIB}/statsmodels/stats/adnorm.py
-${PYSITELIB}/statsmodels/stats/adnorm.pyc
-${PYSITELIB}/statsmodels/stats/adnorm.pyo
+${PYSITELIB}/statsmodels/stats/_adnorm.py
+${PYSITELIB}/statsmodels/stats/_adnorm.pyc
+${PYSITELIB}/statsmodels/stats/_adnorm.pyo
+${PYSITELIB}/statsmodels/stats/_lilliefors.py
+${PYSITELIB}/statsmodels/stats/_lilliefors.pyc
+${PYSITELIB}/statsmodels/stats/_lilliefors.pyo
${PYSITELIB}/statsmodels/stats/anova.py
${PYSITELIB}/statsmodels/stats/anova.pyc
${PYSITELIB}/statsmodels/stats/anova.pyo
@@ -1486,6 +1429,9 @@ ${PYSITELIB}/statsmodels/stats/api.pyo
${PYSITELIB}/statsmodels/stats/base.py
${PYSITELIB}/statsmodels/stats/base.pyc
${PYSITELIB}/statsmodels/stats/base.pyo
+${PYSITELIB}/statsmodels/stats/contingency_tables.py
+${PYSITELIB}/statsmodels/stats/contingency_tables.pyc
+${PYSITELIB}/statsmodels/stats/contingency_tables.pyo
${PYSITELIB}/statsmodels/stats/contrast.py
${PYSITELIB}/statsmodels/stats/contrast.pyc
${PYSITELIB}/statsmodels/stats/contrast.pyo
@@ -1522,9 +1468,9 @@ ${PYSITELIB}/statsmodels/stats/libqsturng/tests/bootleg.dat
${PYSITELIB}/statsmodels/stats/libqsturng/tests/test_qsturng.py
${PYSITELIB}/statsmodels/stats/libqsturng/tests/test_qsturng.pyc
${PYSITELIB}/statsmodels/stats/libqsturng/tests/test_qsturng.pyo
-${PYSITELIB}/statsmodels/stats/lilliefors.py
-${PYSITELIB}/statsmodels/stats/lilliefors.pyc
-${PYSITELIB}/statsmodels/stats/lilliefors.pyo
+${PYSITELIB}/statsmodels/stats/mediation.py
+${PYSITELIB}/statsmodels/stats/mediation.pyc
+${PYSITELIB}/statsmodels/stats/mediation.pyo
${PYSITELIB}/statsmodels/stats/moment_helpers.py
${PYSITELIB}/statsmodels/stats/moment_helpers.pyc
${PYSITELIB}/statsmodels/stats/moment_helpers.pyo
@@ -1534,6 +1480,9 @@ ${PYSITELIB}/statsmodels/stats/multicomp.pyo
${PYSITELIB}/statsmodels/stats/multitest.py
${PYSITELIB}/statsmodels/stats/multitest.pyc
${PYSITELIB}/statsmodels/stats/multitest.pyo
+${PYSITELIB}/statsmodels/stats/multivariate_tools.py
+${PYSITELIB}/statsmodels/stats/multivariate_tools.pyc
+${PYSITELIB}/statsmodels/stats/multivariate_tools.pyo
${PYSITELIB}/statsmodels/stats/outliers_influence.py
${PYSITELIB}/statsmodels/stats/outliers_influence.pyc
${PYSITELIB}/statsmodels/stats/outliers_influence.pyo
@@ -1559,6 +1508,8 @@ ${PYSITELIB}/statsmodels/stats/tests/results/__init__.py
${PYSITELIB}/statsmodels/stats/tests/results/__init__.pyc
${PYSITELIB}/statsmodels/stats/tests/results/__init__.pyo
${PYSITELIB}/statsmodels/stats/tests/results/bootleg.csv
+${PYSITELIB}/statsmodels/stats/tests/results/contingency_table_r_results.csv
+${PYSITELIB}/statsmodels/stats/tests/results/framing.csv
${PYSITELIB}/statsmodels/stats/tests/results/influence_lsdiag_R.json
${PYSITELIB}/statsmodels/stats/tests/results/influence_measures_R.csv
${PYSITELIB}/statsmodels/stats/tests/results/influence_measures_bool_R.csv
@@ -1574,6 +1525,9 @@ ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.pyo
${PYSITELIB}/statsmodels/stats/tests/test_anova.py
${PYSITELIB}/statsmodels/stats/tests/test_anova.pyc
${PYSITELIB}/statsmodels/stats/tests/test_anova.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.py
+${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyo
${PYSITELIB}/statsmodels/stats/tests/test_contrast.py
${PYSITELIB}/statsmodels/stats/tests/test_contrast.pyc
${PYSITELIB}/statsmodels/stats/tests/test_contrast.pyo
@@ -1596,6 +1550,9 @@ ${PYSITELIB}/statsmodels/stats/tests/test_groups_sw.pyo
${PYSITELIB}/statsmodels/stats/tests/test_inter_rater.py
${PYSITELIB}/statsmodels/stats/tests/test_inter_rater.pyc
${PYSITELIB}/statsmodels/stats/tests/test_inter_rater.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_mediation.py
+${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyo
${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.py
${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyc
${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyo
@@ -1635,38 +1592,15 @@ ${PYSITELIB}/statsmodels/stats/tests/test_weightstats.pyo
${PYSITELIB}/statsmodels/stats/weightstats.py
${PYSITELIB}/statsmodels/stats/weightstats.pyc
${PYSITELIB}/statsmodels/stats/weightstats.pyo
-${PYSITELIB}/statsmodels/tests/R_ig.s
-${PYSITELIB}/statsmodels/tests/R_lbw.s
-${PYSITELIB}/statsmodels/tests/__init__.py
-${PYSITELIB}/statsmodels/tests/__init__.pyc
-${PYSITELIB}/statsmodels/tests/__init__.pyo
-${PYSITELIB}/statsmodels/tests/check_for_rpy.py
-${PYSITELIB}/statsmodels/tests/check_for_rpy.pyc
-${PYSITELIB}/statsmodels/tests/check_for_rpy.pyo
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-${PYSITELIB}/statsmodels/tests/coverage_sm.pyc
-${PYSITELIB}/statsmodels/tests/coverage_sm.pyo
-${PYSITELIB}/statsmodels/tests/results/__init__.py
-${PYSITELIB}/statsmodels/tests/results/__init__.pyc
-${PYSITELIB}/statsmodels/tests/results/__init__.pyo
-${PYSITELIB}/statsmodels/tests/results/cancer_resids.csv
-${PYSITELIB}/statsmodels/tests/results/cancerdata.csv
-${PYSITELIB}/statsmodels/tests/results/cancerident_resids.csv
-${PYSITELIB}/statsmodels/tests/results/gaussinvlink_resids.csv.xxx
-${PYSITELIB}/statsmodels/tests/results/gaussinvlinkdata.csv.xxx
-${PYSITELIB}/statsmodels/tests/results/glm_gaussian_log_resid.csv.xxx
-${PYSITELIB}/statsmodels/tests/rmodelwrap.py
-${PYSITELIB}/statsmodels/tests/rmodelwrap.pyc
-${PYSITELIB}/statsmodels/tests/rmodelwrap.pyo
${PYSITELIB}/statsmodels/tools/__init__.py
${PYSITELIB}/statsmodels/tools/__init__.pyc
${PYSITELIB}/statsmodels/tools/__init__.pyo
+${PYSITELIB}/statsmodels/tools/_testing.py
+${PYSITELIB}/statsmodels/tools/_testing.pyc
+${PYSITELIB}/statsmodels/tools/_testing.pyo
${PYSITELIB}/statsmodels/tools/catadd.py
${PYSITELIB}/statsmodels/tools/catadd.pyc
${PYSITELIB}/statsmodels/tools/catadd.pyo
-${PYSITELIB}/statsmodels/tools/compatibility.py
-${PYSITELIB}/statsmodels/tools/compatibility.pyc
-${PYSITELIB}/statsmodels/tools/compatibility.pyo
${PYSITELIB}/statsmodels/tools/data.py
${PYSITELIB}/statsmodels/tools/data.pyc
${PYSITELIB}/statsmodels/tools/data.pyo
@@ -1700,6 +1634,9 @@ ${PYSITELIB}/statsmodels/tools/rootfinding.pyo
${PYSITELIB}/statsmodels/tools/sm_exceptions.py
${PYSITELIB}/statsmodels/tools/sm_exceptions.pyc
${PYSITELIB}/statsmodels/tools/sm_exceptions.pyo
+${PYSITELIB}/statsmodels/tools/testing.py
+${PYSITELIB}/statsmodels/tools/testing.pyc
+${PYSITELIB}/statsmodels/tools/testing.pyo
${PYSITELIB}/statsmodels/tools/tests/__init__.py
${PYSITELIB}/statsmodels/tools/tests/__init__.pyc
${PYSITELIB}/statsmodels/tools/tests/__init__.pyo
@@ -1712,6 +1649,12 @@ ${PYSITELIB}/statsmodels/tools/tests/test_data.pyo
${PYSITELIB}/statsmodels/tools/tests/test_eval_measures.py
${PYSITELIB}/statsmodels/tools/tests/test_eval_measures.pyc
${PYSITELIB}/statsmodels/tools/tests/test_eval_measures.pyo
+${PYSITELIB}/statsmodels/tools/tests/test_grouputils.py
+${PYSITELIB}/statsmodels/tools/tests/test_grouputils.pyc
+${PYSITELIB}/statsmodels/tools/tests/test_grouputils.pyo
+${PYSITELIB}/statsmodels/tools/tests/test_linalg.py
+${PYSITELIB}/statsmodels/tools/tests/test_linalg.pyc
+${PYSITELIB}/statsmodels/tools/tests/test_linalg.pyo
${PYSITELIB}/statsmodels/tools/tests/test_numdiff.py
${PYSITELIB}/statsmodels/tools/tests/test_numdiff.pyc
${PYSITELIB}/statsmodels/tools/tests/test_numdiff.pyo
@@ -1724,15 +1667,30 @@ ${PYSITELIB}/statsmodels/tools/tests/test_rootfinding.pyo
${PYSITELIB}/statsmodels/tools/tests/test_tools.py
${PYSITELIB}/statsmodels/tools/tests/test_tools.pyc
${PYSITELIB}/statsmodels/tools/tests/test_tools.pyo
+${PYSITELIB}/statsmodels/tools/tests/test_transform_model.py
+${PYSITELIB}/statsmodels/tools/tests/test_transform_model.pyc
+${PYSITELIB}/statsmodels/tools/tests/test_transform_model.pyo
+${PYSITELIB}/statsmodels/tools/tests/test_web.py
+${PYSITELIB}/statsmodels/tools/tests/test_web.pyc
+${PYSITELIB}/statsmodels/tools/tests/test_web.pyo
${PYSITELIB}/statsmodels/tools/tools.py
${PYSITELIB}/statsmodels/tools/tools.pyc
${PYSITELIB}/statsmodels/tools/tools.pyo
+${PYSITELIB}/statsmodels/tools/transform_model.py
+${PYSITELIB}/statsmodels/tools/transform_model.pyc
+${PYSITELIB}/statsmodels/tools/transform_model.pyo
+${PYSITELIB}/statsmodels/tools/web.py
+${PYSITELIB}/statsmodels/tools/web.pyc
+${PYSITELIB}/statsmodels/tools/web.pyo
${PYSITELIB}/statsmodels/tools/wrappers.py
${PYSITELIB}/statsmodels/tools/wrappers.pyc
${PYSITELIB}/statsmodels/tools/wrappers.pyo
${PYSITELIB}/statsmodels/tsa/__init__.py
${PYSITELIB}/statsmodels/tsa/__init__.pyc
${PYSITELIB}/statsmodels/tsa/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/_bds.py
+${PYSITELIB}/statsmodels/tsa/_bds.pyc
+${PYSITELIB}/statsmodels/tsa/_bds.pyo
${PYSITELIB}/statsmodels/tsa/adfvalues.py
${PYSITELIB}/statsmodels/tsa/adfvalues.pyc
${PYSITELIB}/statsmodels/tsa/adfvalues.pyo
@@ -1775,6 +1733,12 @@ ${PYSITELIB}/statsmodels/tsa/descriptivestats.pyo
${PYSITELIB}/statsmodels/tsa/filters/__init__.py
${PYSITELIB}/statsmodels/tsa/filters/__init__.pyc
${PYSITELIB}/statsmodels/tsa/filters/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/filters/_utils.py
+${PYSITELIB}/statsmodels/tsa/filters/_utils.pyc
+${PYSITELIB}/statsmodels/tsa/filters/_utils.pyo
+${PYSITELIB}/statsmodels/tsa/filters/api.py
+${PYSITELIB}/statsmodels/tsa/filters/api.pyc
+${PYSITELIB}/statsmodels/tsa/filters/api.pyo
${PYSITELIB}/statsmodels/tsa/filters/bk_filter.py
${PYSITELIB}/statsmodels/tsa/filters/bk_filter.pyc
${PYSITELIB}/statsmodels/tsa/filters/bk_filter.pyo
@@ -1790,12 +1754,15 @@ ${PYSITELIB}/statsmodels/tsa/filters/hp_filter.pyo
${PYSITELIB}/statsmodels/tsa/filters/tests/__init__.py
${PYSITELIB}/statsmodels/tsa/filters/tests/__init__.pyc
${PYSITELIB}/statsmodels/tsa/filters/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/__init__.py
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/filter_results.py
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/filter_results.pyc
+${PYSITELIB}/statsmodels/tsa/filters/tests/results/filter_results.pyo
${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.py
${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyc
${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyo
-${PYSITELIB}/statsmodels/tsa/filters/utils.py
-${PYSITELIB}/statsmodels/tsa/filters/utils.pyc
-${PYSITELIB}/statsmodels/tsa/filters/utils.pyo
${PYSITELIB}/statsmodels/tsa/interp/__init__.py
${PYSITELIB}/statsmodels/tsa/interp/__init__.pyc
${PYSITELIB}/statsmodels/tsa/interp/__init__.pyo
@@ -1811,8 +1778,6 @@ ${PYSITELIB}/statsmodels/tsa/interp/tests/test_denton.pyo
${PYSITELIB}/statsmodels/tsa/kalmanf/__init__.py
${PYSITELIB}/statsmodels/tsa/kalmanf/__init__.pyc
${PYSITELIB}/statsmodels/tsa/kalmanf/__init__.pyo
-${PYSITELIB}/statsmodels/tsa/kalmanf/kalman_loglike.c
-${PYSITELIB}/statsmodels/tsa/kalmanf/kalman_loglike.pyx
${PYSITELIB}/statsmodels/tsa/kalmanf/kalman_loglike.so
${PYSITELIB}/statsmodels/tsa/kalmanf/kalmanfilter.py
${PYSITELIB}/statsmodels/tsa/kalmanf/kalmanfilter.pyc
@@ -1820,9 +1785,142 @@ ${PYSITELIB}/statsmodels/tsa/kalmanf/kalmanfilter.pyo
${PYSITELIB}/statsmodels/tsa/mlemodel.py
${PYSITELIB}/statsmodels/tsa/mlemodel.pyc
${PYSITELIB}/statsmodels/tsa/mlemodel.pyo
-${PYSITELIB}/statsmodels/tsa/setup.py
-${PYSITELIB}/statsmodels/tsa/setup.pyc
-${PYSITELIB}/statsmodels/tsa/setup.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/__init__.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/_hamilton_filter.so
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_autoregression.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_autoregression.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_autoregression.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_regression.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_regression.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_regression.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_switching.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_switching.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/markov_switching.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/__init__.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/mar_filardo.csv
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/results_predict_fedfunds.csv
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_regression.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_regression.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_regression.pyo
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_switching.py
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_switching.pyc
+${PYSITELIB}/statsmodels/tsa/regime_switching/tests/test_markov_switching.pyo
+${PYSITELIB}/statsmodels/tsa/seasonal.py
+${PYSITELIB}/statsmodels/tsa/seasonal.pyc
+${PYSITELIB}/statsmodels/tsa/seasonal.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/_statespace.so
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+${PYSITELIB}/statsmodels/tsa/statespace/api.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/api.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.py
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/kalman_filter.py
+${PYSITELIB}/statsmodels/tsa/statespace/kalman_filter.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/kalman_filter.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/kalman_smoother.py
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+${PYSITELIB}/statsmodels/tsa/statespace/representation.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/representation.pyo
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+${PYSITELIB}/statsmodels/tsa/statespace/sarimax.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/sarimax.pyo
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+${PYSITELIB}/statsmodels/tsa/statespace/structural.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/structural.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/clark1989.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/manufac.dta
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv
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+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_kalman_filter.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_kalman_filter.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_kalman_filter.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_sarimax.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_sarimax.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_sarimax.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_sarimax_coverage.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_structural.py
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+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_structural.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_var_stata.csv
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+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_varmax.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_regression.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_regression.csv
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+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_options.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_prediction.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_prediction.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_prediction.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_representation.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_representation.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_representation.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_sarimax.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_sarimax.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_sarimax.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_smoothing.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_smoothing.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_smoothing.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_structural.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_structural.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_structural.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_tools.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_tools.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_tools.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_varmax.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_varmax.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_varmax.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tools.py
+${PYSITELIB}/statsmodels/tsa/statespace/tools.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tools.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/varmax.py
+${PYSITELIB}/statsmodels/tsa/statespace/varmax.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/varmax.pyo
${PYSITELIB}/statsmodels/tsa/stattools.py
${PYSITELIB}/statsmodels/tsa/stattools.pyc
${PYSITELIB}/statsmodels/tsa/stattools.pyo
@@ -1873,6 +1971,8 @@ ${PYSITELIB}/statsmodels/tsa/tests/results/arima211nc_results.py
${PYSITELIB}/statsmodels/tsa/tests/results/arima211nc_results.pyc
${PYSITELIB}/statsmodels/tsa/tests/results/arima211nc_results.pyo
${PYSITELIB}/statsmodels/tsa/tests/results/arima212_forecast.csv
+${PYSITELIB}/statsmodels/tsa/tests/results/bds_data.csv
+${PYSITELIB}/statsmodels/tsa/tests/results/bds_results.csv
${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.py
${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.pyc
${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.pyo
@@ -1925,12 +2025,21 @@ ${PYSITELIB}/statsmodels/tsa/tests/test_arima.pyo
${PYSITELIB}/statsmodels/tsa/tests/test_arima_process.py
${PYSITELIB}/statsmodels/tsa/tests/test_arima_process.pyc
${PYSITELIB}/statsmodels/tsa/tests/test_arima_process.pyo
+${PYSITELIB}/statsmodels/tsa/tests/test_bds.py
+${PYSITELIB}/statsmodels/tsa/tests/test_bds.pyc
+${PYSITELIB}/statsmodels/tsa/tests/test_bds.pyo
+${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.py
+${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.pyc
+${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.pyo
${PYSITELIB}/statsmodels/tsa/tests/test_stattools.py
${PYSITELIB}/statsmodels/tsa/tests/test_stattools.pyc
${PYSITELIB}/statsmodels/tsa/tests/test_stattools.pyo
${PYSITELIB}/statsmodels/tsa/tests/test_tsa_tools.py
${PYSITELIB}/statsmodels/tsa/tests/test_tsa_tools.pyc
${PYSITELIB}/statsmodels/tsa/tests/test_tsa_tools.pyo
+${PYSITELIB}/statsmodels/tsa/tests/test_x13.py
+${PYSITELIB}/statsmodels/tsa/tests/test_x13.pyc
+${PYSITELIB}/statsmodels/tsa/tests/test_x13.pyo
${PYSITELIB}/statsmodels/tsa/tsatools.py
${PYSITELIB}/statsmodels/tsa/tsatools.pyc
${PYSITELIB}/statsmodels/tsa/tsatools.pyo
@@ -1976,6 +2085,9 @@ ${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/__init__.pyo
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar.py
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar.pyc
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar.pyo
+${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py
+${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar_st.pyc
+${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_svar_st.pyo
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_var.py
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_var.pyc
${PYSITELIB}/statsmodels/tsa/vector_ar/tests/results/results_var.pyo
@@ -1995,6 +2107,9 @@ ${PYSITELIB}/statsmodels/tsa/vector_ar/util.pyo
${PYSITELIB}/statsmodels/tsa/vector_ar/var_model.py
${PYSITELIB}/statsmodels/tsa/vector_ar/var_model.pyc
${PYSITELIB}/statsmodels/tsa/vector_ar/var_model.pyo
+${PYSITELIB}/statsmodels/tsa/x13.py
+${PYSITELIB}/statsmodels/tsa/x13.pyc
+${PYSITELIB}/statsmodels/tsa/x13.pyo
${PYSITELIB}/statsmodels/version.py
${PYSITELIB}/statsmodels/version.pyc
${PYSITELIB}/statsmodels/version.pyo
diff --git a/py-statsmodels/distinfo b/py-statsmodels/distinfo
index 1bf0d28..dac9a6e 100644
--- a/py-statsmodels/distinfo
+++ b/py-statsmodels/distinfo
@@ -1,5 +1,6 @@
$NetBSD: distinfo,v 1.1 2014/07/26 11:16:19 jihbed Exp $
-SHA1 (statsmodels-0.5.0.tar.gz) = 4f632d515c2fb1f2f796bf335c86ffa89cd26852
-RMD160 (statsmodels-0.5.0.tar.gz) = b9f7322c671e75798187fa693f21d20f51050318
-Size (statsmodels-0.5.0.tar.gz) = 5519668 bytes
+SHA1 (statsmodels-0.8.0rc1.tar.gz) = 808bddc2a04e2429c76fb594da05fd2952996292
+RMD160 (statsmodels-0.8.0rc1.tar.gz) = d2547e06657deac9270a9d6c972a4e042b4ffeb3
+SHA512 (statsmodels-0.8.0rc1.tar.gz) = e963c7fc98a8d087df43ef0b6f7807dc25d67713fdf5a74391979604291f194e088ac0669465f6c94da867f58872fcee6cafdfb3ad9d85ce4c903316351734ee
+Size (statsmodels-0.8.0rc1.tar.gz) = 9384625 bytes
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