Author: Tristan Senga Kiess\'{e}, Silvio S. Zocchi, C\'{e}lestin C. Kokonendji
Maintainer: Tristan Senga Kiess\'{e} <tristan.sengakiesse@univ-pau.fr>
Description: This is a package for discrete triangular distributions which complete the classical discrete distributions like binomial, Poisson and Negative binomial.
Packaged: Fri Mar  6 18:46:11 2009; root

Author: Tristan Senga Kiess\'{e}, Silvio S. Zocchi, C\'{e}lestin C.
Description: This is a package for discrete triangular distributions
which complete the classical discrete distributions like
binomial, Poisson and Negative binomial.
Packaged: Fri Mar 13 13:47:05 2009; root

Title: Symmetric discrete triangular distributions
Maintainer: Tristan Senga Kiess\'{e} <tristan.senga-kiesse@lcpc.fr>
Description: This is a package for symmetric discrete triangular

Title: General discrete triangular distribution
Author: Tristan Senga Kiess\'{e}, Francial G. Libengu\'e, Silvio S.
Description: This is a package for general discrete triangular
distributions which is an extension of the symmetric discrete

Title: Temporally Regularized Matrix Factorization
Description: Use alternating least squares to estimate a Temporally Regularized Matrix Factorization (Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon, 2015) <arXiv:1509.08333v3>
(possibly) in the presence of missing values.
Packaged: 2021-01-18 22:25:43 UTC; chammerquist

Description: Functions to estimate temporally regularized matrix factorization (TRMF) for forecasting and imputing values in short but high-dimensional time series. Uses regularized alternating least squares to compute the factorization, allows for several types of constraints on matrix factors and can handle weighted data.
Packaged: 2021-05-17 21:26:17 UTC; chammerquist

Packaged: 2021-05-19 16:15:05 UTC; chammerquist

Description: Functions to estimate temporally regularized matrix factorizations (TRMF) for forecasting and imputing values in short but high-dimensional time series. Uses regularized alternating least squares to compute the factorization, allows for several types of constraints on matrix factors and natively handles weighted and missing data.
Packaged: 2021-05-24 16:39:21 UTC; chammerquist