Detection of migration events and segments of continuous residence based on irregular time series of location data as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.
Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports APSystems', Enphase', and Fronius microinverters.
Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.
This package provides summarized MinION
sequencing data for Salmonella Typhi published by Ashton et al. in 2015. Three replicate runs are each provided as Fast5Summary
objects.
Missing data imputation based on the missForest
algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest()
is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict()
is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing
data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>
.
Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way.