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intercompany_trade_purchase_order_reorder_lines

Author: GRAP
License: AGPL-3
Branch: 7.0
Repository: erick-alvino/odoo-addons-intercompany-trade
Dependencies: intercompany_trade_purchase_sale, and purchase_order_reorder_lines
Languages: PO File (18, 32.7%), and Python (37, 67.3%)


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l10n_es_aeat_mod303_special_prorate_regularization_capital_asset_auc

Author: NuoBiT Solutions, S.L.
License: AGPL-3
Branch: 14.0
Repository: nuobit/odoo-addons
Dependencies: account_asset_management, account_asset_transfer_extension, and l10n_es_aeat_mod303_special_prorate_regularization_capital_asset
Languages: Python (78, 83.0%), and reStructuredText (16, 17.0%)

This module allows to regularize transferred capital assets prorate differences on 303 report
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account_cancel_invoice_check_payment_order_unported

Author: Camptocamp
License: no license
Branch: master
Repository: mdietrichc2c/account-financial-tools
Dependencies: account, account_cancel, and account_payment
Languages: PO File (63, 64.3%), and Python (35, 35.7%)


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l10n_es_aeat_mod303_special_prorate_regularization_capital_asset_legacy

Author: NuoBiT Solutions, S.L.
License: AGPL-3
Branch: 14.0
Repository: nuobit/odoo-addons
Dependencies: l10n_es_aeat, and l10n_es_aeat_mod303_special_prorate_regularization_capital_asset
Languages: PO File (30, 17.1%), Python (105, 60.0%), XML (24, 13.7%), and reStructuredText (16, 9.1%)

This module extends Special Prorate Regularization Capital Asset to include old assets
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cenit_350000plus_recipe_and_grocery_list_api_v2_partner

Author: Cenit IO
License: no license
Branch: 9.0
Repository: connect-to-ak/odoo-integrations
Dependencies: base, and cenit_base
Languages: HTML (38, 13.8%), Python (69, 25.0%), and XML (169, 61.2%)

#Documentation This is the documentation for the partner endpoint of the BigOven Recipe and Grocery List API....
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cenit_firebrowse_beta_api_1_1_33_2016_06_13_21_20_41_eb41119bbe78d5f930199104

Author: Cenit IO
License: no license
Branch: 9.0
Repository: connect-to-ak/odoo-integrations
Dependencies: cenit_base
Languages: HTML (38, 46.9%), Python (37, 45.7%), and XML (6, 7.4%)

A simple and elegant way to explore cancer data
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