Access Scottish Health and Social Care Open Data
Allows potential users of Scottish Health and Social Care Open Data
(<https://www.opendata.nhs.scot/>) to easily explore and extract the
available data.
glue, httr, utils, data.table, methods, jsonlite
knitr, rmarkdown, testthat (>= 3.0.0), mockery, digest

Functions to compute the one-sided dynamic
principal components ('odpc') introduced in Smucler, Peña and Yohai (2017)
<arXiv:1708.04705>. 'odpc' is a novel dimension
reduction technique for multivariate time series, that is useful
for forecasting. These dynamic principal components are defined as
the linear combinations of the present and past values of the series
that minimize the reconstruction mean squared error.
methods, Rcpp (>= 0.12.7), gdpc, forecast

Functions to compute the one-sided dynamic
principal components ('odpc') introduced in Smucler, Peña and Yohai (2018)
<DOI:10.1080/01621459.2018.1520117>. 'odpc' is a novel dimension
reduction technique for multivariate time series, that is useful
for forecasting. These dynamic principal components are defined as
the linear combinations of the present and past values of the series
that minimize the reconstruction mean squared error.
methods, Rcpp (>= 0.12.7), forecast, parallel, doParallel,
foreach

Functions to compute the one-sided dynamic
principal components ('odpc') introduced in Peña, Smucler and Yohai (2019)
<DOI:10.1080/01621459.2018.1520117>. 'odpc' is a novel dimension
reduction technique for multivariate time series, that is useful
for forecasting. These dynamic principal components are defined as
the linear combinations of the present and past values of the series
that minimize the reconstruction mean squared error.
methods, Rcpp (>= 0.12.7), forecast, parallel, doParallel,
foreach, MASS

Optimal design and performance of random intercept and slope
models.
Martijn van de Pol <m.van.de.pol@myscience.eu>
Simulation functions to assess the optimal design and
performance of random intercept and slope models (i.e. mixed
models), which can be used to a priori determine adequate
sampling designs for e.g. reaction norm studies. Functions
allow users to vary the sampling design in terms of number of
grouping units sampled (e.g. individuals, schools, populations,
etc.) and replicates per grouping unit (unbalanced as well
balanced datasets) and also allow users to vary the parameter
conditions used to generate the data. Subsequently, the
performance of mixed models (based on lme4 package) fitted on
these datasets is assessed in terms of the accuracy and the
precision of estimates of fixed and random parameter, as well
as the statistical power.
R (>= 2.11.0), lme4, mvtnorm, fields, graphics