# FoReco 1.2.0

* (CRAN Package Check Results) Fixed compatibility issues with [`osqp`](https://CRAN.R-project.org/package=osqp) 1.0. The package is now fully compatible with any version.
* Added `csmvn()`, `temvn()`, and `ctmvn()` for Gaussian probabilistic forecast reconciliation in the cross-sectional, temporal, and cross-temporal frameworks using the [`distributional`](https://CRAN.R-project.org/package=distributional) package;
* Added `cssmp()`, `tesmp()`, and `ctsmp()` for sample-based probabilistic forecast reconciliation in the cross-sectional, temporal, and cross-temporal frameworks using the [`distributional`](https://CRAN.R-project.org/package=distributional) package;
* Added `as_ctmatrix()` and `as_horizon_stacked_ctmatrix()` functions to convert between horizon-stacked (cross-temporal version) and cross-temporal layouts;
* Added `as_tevector()` and `as_horizon_stacked_tematrix()` functions to convert between horizon-stacked (temporal version) and temporal layouts;
* Added non-negative forecast reconciliation algorithms *bpv* (block principal pivoting algorithm), *nfca* (negative forecasts correction algorithm), *nnic* (iterative non-negative reconciliation with immutable constraints), and *sntz* (set-negative-to-zero with bottom-up and top-down alternatives) based on:
  * Girolimetto, D. (2025), Non-negative forecast reconciliation: Optimal methods and operational solutions. *arXiv*;
  * Kourentzes, N. and Athanasopoulos, G. (2021) Elucidate structure in intermittent demand series. *European Journal of Operational Research*, 288, 141-152. [doi:10.1016/j.ejor.2020.05.046](https://doi.org/10.1016/j.ejor.2020.05.046);
  * Wickramasuriya, S. L., Turlach, B. A., and Hyndman, R. J. (2020), "Optimal non-negative forecast reconciliation", *Statistics and Computing*, 30(5), 1167–1182. [doi:10.1007/s11222-020-09930-0](https://doi.org/10.1007/s11222-020-09930-0);
* Added `...` for `simulate()` additional arguments in `csboot()`, `teboot()` and `ctboot()`;
* Fixed bugs and improved stability.

# FoReco 1.1.0

### New Features

* Added new `bpv` non-negative forecast reconciliation algorithm (experimental) based on 
  the cross-sectional framework presented in Wickramasuriya et al. (2020) and, now, extended 
  for the temporal and cross-temporal framework:
  * Wickramasuriya, S. L., Turlach, B. A., and Hyndman, R. J. (2020), "Optimal non-negative 
    forecast reconciliation", *Statistics and Computing*, 30(5), 1167–1182. 
    [doi:10.1007/s11222-020-09930-0](https://doi.org/10.1007/s11222-020-09930-0);
* New `oasd` cross-sectional covariance matrix (experimental), implementing an oracle shrunk 
  covariance estimation (Ando and Xiao, 2023):
  * Ando, S., and Xiao, M. (2023), "High-dimensional covariance matrix estimation: shrinkage 
    toward a diagonal target", *IMF Working Papers*, 2023(257), A001;
* Redesigned `bounds` parameter to enable bounded forecast reconciliation for `csrec()`, 
  `terec()`, and `ctrec()` functions;
* Introduced new `set_bounds()` function to define custom bounds for reconciliation.

### Bug Fixes

* Fixed bug when only a subset of `agg_order` factors was selected in `iterec()`, `tcsrec()`, 
  and `cstrec()` functions.

# FoReco 1.0.0

> **Note** – The latest release of FoReco introduces significant changes to its function 
> notation and adds several new features. This major update, FoReco 1.0, is not compatible 
> with previous versions due to the substantial changes made to the package's core structure. 
> The previous version is available on [Github](https://github.com/danigiro/FoReco026) 
> ([docs](https://danigiro.github.io/FoReco026/)).
>
> Due to the significant changes in FoReco 1.0, users are advised to carefully review the 
> updated documentation and examples before using the new version. The latest documentation 
> and release notes are available on 
> [danigiro.github.io/FoReco/](https://danigiro.github.io/FoReco/)


* **Updated Function Notation:** All functions related to cross-sectional, temporal, and 
  cross-temporal frameworks now use the prefixes **cs**, **te**, and **ct**, respectively. 
  For example, the optimal combination reconciliation functions are now `csrec()`, `terec()`, 
  and `ctrec()`.

* **Simplified Function Outputs:** Reconciliation functions now return only matrices. 
  Additional information can be accessed using `attr(., "FoReco")` or the `recoinfo()` 
  function.

* **New Datasets:** Two new datasets, `itagdp` (Italian Quarterly National Accounts) and 
  `vndata` (Australian Tourism Demand), are included along with their respective aggregation 
  or constraint matrices.

* **Classic Approach:** The middle-out approach (`csmo()`, `temo()`, and `ctmo()`) has been 
  implemented alongside the classic bottom-up (`csbu()`, `tebu()`, and `ctbu()`) and top-down 
  (`cstd()`, `tetd()`, and `cttd()`) methods.

* **Level Conditional Coherent Reconciliation:** Level conditional coherent reconciliation 
  is now available for all constraints: `cslcc()` (cross-sectional), `telcc()` (temporal), 
  and `ctlcc()` (cross-temporal).

* **Immutable reconciliation:** The `immutable()` parameter has been added to the 
  reconciliation functions (`csrec()`, `terec()`, and `ctrec()`) to prevent the base 
  forecasts from being modified with both the structural (`approach='strc'`) and the 
  zero-constrained (`approach='proj'`) approach.

* **Balanced and unbalanced hierarchy:** added `balance_hierarchy()` and `unbalance_hierarchy()` 
  for dealing with balanced and unbalanced hierarchies. 

* **Projection Matrix Functions:** Functions `csprojmat()`, `teprojmat()`, and `ctprojmat()` 
  have been added to obtain projection matrices.

* **Covariance Matrix Functions:** Functions `cscov()`, `tecov()`, and `ctcov()` have been 
  added to obtain covariance matrices.

* **Function Renaming:** Several functions have been renamed to improve consistency and clarity

    - `Cmatrix()` -> `df2aggmat()`
    - `hts_tools()` -> `cstools()`
    - `thf_tools()` -> `tetools()`
    - `ctf_tools()` -> `cttools()`
    - `agg_ts()` -> `aggts()`
    - `residuals_matrix()` -> `res2matrix()`
    - `boot_cs()` -> `csboot()`
    - `boot_te()` -> `teboot()`
    - `boot_ct()` -> `ctboot()`
    - `htsrec()` -> `csrec()`
    - `thfrec()` -> `terec()`
    - `octrec()` -> `ctrec()`
    - `lccrec()` -> `cslcc()`


# FoReco 0.2.6

##### Major changes: probabilistic forecast reconciliation
* Added `boot_cs()`, `boot_te()` and `boot_ct()` to draw samples from, respectively, 
  cross-sectional, temporal and cross-temporal joint (block) bootstrap.

##### Minor changes
* Fixed deprecation warnings in [Matrix](https://CRAN.R-project.org/package=Matrix) (v. 1.5-0);
* Improved docs and bug fixes;
* Fixed `ctbu()` inputs;
* Added `FoReco2matrix()` to transform FoReco forecasts input and output in a list 
  of matrix/vector class;
* Added `agg_ts()`: non-overlapping temporal aggregation of a time series according 
  to a specific aggregation order;
* Added `arrange_hres()` and `residuals_matrix()` functions to arrange residuals for 
  the covariance matrix under Gaussianity.

# FoReco 0.2.5

##### Minor changes
* Fixed the not negative reconciliation "**sntz**" for `octrec()`;
* Fixed documentation.

# FoReco 0.2.4

##### Major changes
* Added `lcmat()` function.

##### Minor changes
* Fixed BU approach when the number of columns of basef is equal to the number of 
  bottom time series `htsrec()`;
* Fixed `score_index()`;
* Fixed the `bounds` param when `type = "S"` in `htsrec()`, `thfrec()` and `octrec()`;
* Add the possibility to fix base forecasts through the `v` param in `htsrec()`, 
  `thfrec()` and `octrec()` - experimental;
* Add two new type of optimal cross-temporal reconciliation (**cs_struc** and 
  **t_struc**);
* Improved docs and bug fixes.

# FoReco 0.2.2

##### Minor changes
* Fixed documentation;
* Removed `ut2c()` and `srref()`.

# FoReco 0.2.1

##### Minor changes
* Fixed a bug in the output of `lccrec()` (now the function returns the Level Conditional 
  Coherent or the Combined Conditional Coherent forecasts);
* Fixed the not negative reconciliation "**KAnn**" when `keep = "list"`.

# FoReco 0.2.0

##### Major changes
* It's possible to use a subset of factors of *m* (max. order of temporal aggregation);
* Added the possibility for `htsrec()`, `thfrec()` and `octrec()` to introduce a list 
  of *h* covariance matrices in the parameters `W` and `Omega`, where *h* stands for 
  the forecast horizon (note that for `thfrec()` and `octrec()` this is the forecast 
  horizon of the entire cycle);
* Param `Sstruc` is no more avaible in `octrec()` and `ctf_tools()`. FoReco uses a fast 
  algorithm to compute **Scheck**, so no external input is needed;
* Modified output of `ctf_tools()` (added `Ccheck`, `Htcheck`, `Scheck`, removed `Cstruc`, 
  `Sstruc`), `hts_tools()` (added `C`) and `thf_tools()` (added `m`);
* Added two new not negative reconciliation techniques ("**KAnn**" and "**sntz**") with a 
  new parameter (`nn_type`) in `htsrec()`, `thfrec()` and `octrec()`;
* Added the top-down reconciliation function `tdrec()`;
* Added the level conditional forecast reconciliation (with and without not-negative 
  constraints) for genuine hierarchical/grouped time series `levrec()` (cross-sectional, 
  temporal and cross-temporal).

##### Minor changes
* Now in `octrec()` it is also possible to introduce the **Ω** covariance matrix variant 
  through the `Omega` parameter and not only the **W** variant with the `W` parameter;
* Updated `tcsrec()`, `cstrec()` and `iterec()`. In the `iterec()` function the `maxit` 
  parameter has been replaced by `itmax`, however for the moment `maxit` is still supported;
* Now FoReco removes null rows from the cross-sectional aggregation matrix **C** and it 
  warns the user if the balanced version of an unbalanced hierarchy is considering duplicated 
  variables;
* Redesigned the console output and added a new convergence norm as *default* for 
  `iterec()` (`norm` parameter).

##### Experimental
* Add the possibility to introduce constraints through the `bounds` param in `htsrec()`, 
  `thfrec()` and `octrec()`;
* Add a function `oct_bounds()` to organize the bounds on a specific dimension (i.e. only 
  cross-sectional or only temporal) in a cross-temporal framework;
* Added `ut2c()` and `srref()` to develop a cross-sectional structural representation starting 
  from a zero constraints kernel matrix;
* Added in `score_index()` the calculation of multiple forecast horizons index (like 1:6) and 
  multiple cross-sectional levels for a forecasting experiment.


# FoReco 0.1.1
Minore release, fixing some bugs and the documentation

* Fixed a bug in `iterec()` when calculating the incoherence
* Fixed documentation 
* Changed the contact mail (now it's daniele.girolimetto@phd.unipd.it)
* Corrected the second section of the vignette "`Average relative accuracy indices`"

# FoReco 0.1.0

* Release on github
