Border and Area Estimation of Data Measured with Additive Error (LeArEst)
Provides methods for estimating borders of uniform distribution on the interval (one-dimensional) and on the elliptical domain (two-dimensional) under measurement errors. For one-dimensional case, it also estimates the length of underlying uniform domain and tests the hypothesized length against two-sided or one-sided alternatives. For two-dimensional case, it estimates the area of underlying uniform domain. It works with numerical inputs as well as with pictures in JPG format.

Extension to ‘spatstat’ for Local Composite Likelihood (spatstat.local)
Extension to the ‘spatstat’ package, enabling the user to fit point process models to point pattern data by local composite likelihood (‘geographically weighted regression’).

R Interface to the Keras Deep Learning Library (kerasR)
Provides a consistent interface to the ‘Keras’ Deep Learning Library directly from within R. ‘Keras’ (see <> for more information) provides specifications for describing dense neural networks, convolution neural networks (CNN) and recurrent neural networks (RNN) running on top of either ‘TensorFlow’ (<https://…/> ) or ‘Theano’ (<http://…/> ). Type conversions between Python and R are automatically handled correctly, even when the default choices would otherwise lead to errors. Includes complete R documentation and many working examples.

Wrappers for ‘tidyr::gather()’ and ‘tidyr::spread()’ (cdata)
Supplies deliberately verbose wrappers for ‘tidyr::gather()’ and ‘tidyr::spread()’, and an explanatory vignette. Useful for training and for enforcing preconditions.