TIFF I/O for ‘ImageJ’ Users (ijtiff)
Correctly import TIFF files that were saved from ‘ImageJ’ and write TIFF files than can be correctly read by ‘ImageJ’ <https://…/>. Full support for TIFF files with floating point (real-numbered) pixels. Also supports text image I/O.

Matrix Exponential using Krylov Subspace Routines (kexpmv)
Implements functions from ‘EXPOKIT’ (<https://…/> ) to calculate matrix exponentials, Sidje RB, (1998) <doi:10.1145/285861.285868>. Includes functions for small dense matrices along with functions for large sparse matrices. The functions for large sparse matrices implement Krylov subspace methods which help minimise the computational complexity for matrix exponentials. ‘Kexpmv’ can be utilised to calculate both the matrix exponential in isolation along with the product of the matrix exponential and a vector.

Computation of Sparse Eigenvectors of a Matrix (sparseEigen)
Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). ‘Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation,’ IEEE Transactions on Signal Processing <doi:10.1109/TSP.2016.2605073>.

Wrapper for the ‘Datamuse’ API to Find Rhyming and Associated Words (rhymer)
Wrapper for ‘Datamuse’ API to find rhyming and other associated words. This includes words of similar meaning, spelling, or other related words. Learn more about the ‘Datamuse’ API here <http://…/>.

ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression (ZOIP)
The ZOIP distribution (Zeros Ones Inflated Proportional) is a proportional data distribution inflated with zeros and/or ones, this distribution is defined on the most known proportional data distributions, the beta and simplex distribution, Jørgensen and Barndorff-Nielsen (1991) <doi:10.1016/0047-259X(91)90008-P>, also allows it to have different parameterizations of the beta distribution, Ferrari and Cribari-Neto (2004) <doi:10.1080/0266476042000214501>, Rigby and Stasinopoulos (2005) <doi:10.18637/jss.v023.i07>. The ZOIP distribution has four parameters, two of which correspond to the proportion of zeros and ones, and the other two correspond to the distribution of the proportional data of your choice. The ‘ZOIP’ package allows adjustments of regression models for fixed and mixed effects for proportional data inflated with zeros and/or ones.

Utilities Parsing ‘HMMER’ Results (rhmmer)
HMMER’ is a profile hidden Markov model tool used primarily for sequence analysis in bioinformatics (<http://…/> ). ‘rhmmer’ provides utilities for parsing the ‘HMMER’ output into tidy data frames.