Description: Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Description: Interacts with the 'Glassdoor' API
<https://www.glassdoor.com/developer/index.htm>. Allows the user to
search job statistics, employer statistics, and job progression,
where 'Glassdoor' provides a breakdown of other jobs a person did
Packaged: 2017-09-14 17:42:26 UTC; johnmuschelli

Packaged: 2018-11-21 16:51:06 UTC; johnmuschelli

Author: Jerome Friedman and R. Tibshirani
Packaged: Mon Nov 26 07:25:41 2007; tibs

Title: Graphical Lasso: Estimation of Gaussian Graphical Models
Author: Jerome Friedman, Trevor Hastie and Rob Tibshirani
Description: Estimation of a sparse inverse covariance matrix using a lasso (L1)
penalty. Facilities are provided for estimates along a path of values

Packaged: 2019-09-24 21:37:23 UTC; robtibshirani

Title:  Graphical lasso- estimation of Gaussian graphical models
Packaged: Wed Jun 18 23:50:37 2008; tibs