It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, 
 it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. 
 The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.

Implements an efficient O(n) algorithm based on bucket-sorting for 
    fast computation of standard clustering comparison measures. Available measures
    include adjusted Rand index (ARI), normalized information distance (NID), 
    normalized mutual information (NMI), adjusted mutual information (AMI), 
    normalized variation information (NVI) and entropy, as described in Vinh et al (2009) 
    <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, 
    a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> 
    and simple Chi-square distance since version 1.0.0.

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.

Estimating and analyzing auto regressive integrated moving average
    (ARIMA) models. The primary function in this package is arima(), which fits 
    an ARIMA model to univariate time series data using a random restart
    algorithm. This approach frequently leads to models that have model 
    likelihood greater than or equal to that of the likelihood obtained by 
    fitting the same model using the arima() function from the 'stats' package. 
    This package enables proper optimization of model likelihoods, which is a 
    necessary condition for performing likelihood ratio tests. This package 
    relies heavily on the source code of the arima() function of the 'stats' 
    package. For more information, please see Jesse Wheeler and Edward L. 
    Ionides (2025) <doi:10.1371/journal.pone.0333993>.