Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.

Tools for analysing inflation dynamics. Computes weighted
    contributions of price index components, core inflation measures
    (trimmed mean, weighted median, exclusion-based) following Bryan and
    Cecchetti (1994) <doi:10.1016/0304-3932(94)90030-2>, inflation
    persistence via sum-of-AR-coefficients, diffusion indices, Phillips
    curve estimation, breakeven inflation, and trend inflation using the
    Beveridge-Nelson decomposition and Hodrick-Prescott filter. All
    functions are pure computation and work with price data from any
    source.

Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .
   Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .

Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. 
    McCracken (2022) <doi:10.1101/2022.12.30.522131>.

Provides methods for detecting influential subjects in
    longitudinal data, particularly when observations are collected at
    irregular time points. The package identifies subjects whose response
    trajectories deviate substantially from population-level patterns,
    helping to diagnose anomalies and undue influence on model estimates.

Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.