Description: Classical (bottom-up and top-down), optimal and heuristic combination forecast
reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly
Imports: cli, corpcor, methods, mathjaxr

constrained time series (Di Fonzo and Girolimetto, 2021, <doi:10.1016/j.ijforecast.2021.08.004>).

constrained time series (Di Fonzo and Girolimetto, 2021) <doi:10.1016/j.ijforecast.2021.08.004>.

Title: Functions from the 2nd Italian Forest Inventory (INFC)
Author: Nicola Puletti, Marco Mura, Cristiano Castaldi, Maurizio Marchi, Ugo
Description: This package provides estimates of tree volume and biomass from
Packaged: 2014-07-05 06:45:25 UTC; nicolapuletti

Title: Functions to Estimate Tree Volume and Phytomass in the Italian
Description: Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and
phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/>)
are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates
accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment.
Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species
identification is now based on EPPO species codes (<https://data.eppo.int/>).
Imports: magrittr, dplyr, purrr, tibble, tidyr, RColorBrewer, metR,
Author: Nicola Puletti [aut, cre] (<https://orcid.org/0000-0002-2142-959X>),

Title: Imputation of missing values through a forward imputation
Author: Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
Description: Imputation of missing values in datasets of ordinal
variables through a forward imputation algorithm

Author: Alessandro Barbiero, Pier Alda Ferrari, Giancarlo Manzi
Packaged: 2013-01-31 07:46:46 UTC; Alessandro

Title: Imputation of Missing Values Through a Forward Imputation
Description: Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm