| mvabund: Statistical methods for analysing multivariate abundance data Y Wang, U Naumann, D Eddelbuettel, J Wilshire, D Warton, J Byrnes, ... R package version 4 (3), 2020 | 298 | 2020 |
| gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models in r J Niku, FKC Hui, S Taskinen, DI Warton Methods in Ecology and Evolution 10 (12), 2173-2182, 2019 | 176 | 2019 |
| Generalized linear latent variable models for multivariate count and biomass data in ecology J Niku, DI Warton, FKC Hui, S Taskinen Journal of Agricultural, Biological and Environmental Statistics 22, 498-522, 2017 | 91 | 2017 |
| Efficient estimation of generalized linear latent variable models J Niku, W Brooks, R Herliansyah, FKC Hui, S Taskinen, DI Warton PloS one 14 (5), e0216129, 2019 | 68 | 2019 |
| gllvm: Generalized linear latent variable models J Niku, W Brooks, R Herliansyah, FKC Hui, S Taskinen, DI Warton, ... R package version 1 (3), 2020 | 31 | 2020 |
| Analyzing environmental‐trait interactions in ecological communities with fourth‐corner latent variable models J Niku, FKC Hui, S Taskinen, DI Warton Environmetrics 32 (6), e2683, 2021 | 17 | 2021 |
| Mvabund: statistical methods for analysing multivariate abundance data. Version 4.1. 6 Y Wang, U Naumann, D Eddelbuettel, J Wilshire, D Warton, J Byrnes, ... | 12 | 2020 |
| gllvm: generalized linear latent variable models. R package version 1.1. 7; 2019 J Niku, W Brooks, R Herliansyah, FKC Hui, S Taskinen, DI Warton | 8 | 2023 |
| Fast and universal estimation of latent variable models using extended variational approximations P Korhonen, FKC Hui, J Niku, S Taskinen Statistics and Computing 33 (1), 26, 2023 | 7 | 2023 |
| Package ‘gllvm.’ J Niku, W Brooks, R Herliansyah, FKC Hui, S Taskinen, DI Warton, ... R Project 326, 2017 | 6 | 2017 |
| A comparison of joint species distribution models for percent cover data P Korhonen, FKC Hui, J Niku, S Taskinen, B van der Veen Methods in Ecology and Evolution 15 (12), 2359-2372, 2024 | 4 | 2024 |
| A large-scale and long-term experiment to identify effectiveness of ecosystem restoration M Elo, S Kareksela, O Ovaskainen, N Abrego, J Niku, S Taskinen, ... bioRxiv, 2024.04. 02.587693, 2024 | 4 | 2024 |
| gllvm: Generalized Linear Latent Variable Models. R package version 1.4. 1 J Niku, W Brooks, R Herliansyah, FKC Hui, P Korhonen, S Taskinen, ... | 3 | 2023 |
| Testate amoebae community analysis as a tool to assess biological impacts of peatland use E Daza Secco, J Haimi, H Högmander, S Taskinen, J Niku, K Meissner Wetlands Ecology and Management 26, 597-611, 2018 | 3 | 2018 |
| On modeling multivariate abundance data with generalized linear latent variable models J Niku JYU dissertations, 2020 | 1 | 2020 |
| Fungal communities associated with arcto-alpine plants are strongly shaped by regional effects M Kumar, J Niku, L Antonielli, G Brader, A Sessitsch, S Taskinen, J Dirk, ... Kumar MGK. Biogeographical diversity of plantassociated microbes in arcto …, 2016 | 1 | 2016 |
| Latenttiin muuttujamalliin perustuva ordinaatiomenetelmä J Niku | 1 | 2015 |
| Wind Is a Primary Driver of Fungal Dispersal Across a Mainland‐Island System D Naranjo‐Orrico, O Ovaskainen, B Furneaux, J Purhonen, PA Arancibia, ... Molecular Ecology, e17675, 2025 | | 2025 |
| Restoration of forestry-drained boreal peatland ecosystems can effectively stop and reverse ecosystem degradation M Elo, S Kareksela, O Ovaskainen, N Abrego, J Niku, S Taskinen, ... Communications Earth & Environment 5 (1), 680, 2024 | | 2024 |
| Author Affiliations M Kumar, A Sessitsch, A Mäki, G Brader, JD van Elsas, J Niku, L Antonielli Journal of Earth System Science 133, 2024 | | 2024 |
