These tools implement in R a fundamental part of the software
    'PACTA' (Paris Agreement Capital Transition Assessment), which is a
    free tool that calculates the alignment between financial portfolios
    and climate scenarios (<https://www.transitionmonitor.com/>). Financial
    institutions use 'PACTA' to study how their capital allocation
    decisions align with climate change mitigation goals. This package
    matches data from corporate lending portfolios to asset level data
    from market-intelligence databases (e.g. power plant capacities,
    emission factors, etc.). This is the first step to assess if a
    financial portfolio aligns with climate goals.

Create plots to visualize the alignment of a corporate
    lending financial portfolio to climate change scenarios based on
    climate indicators (production and emission intensities) across key
    climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital 
    Transition Assessment; <https://www.transitionmonitor.com/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.

Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.

Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow the display of fireworks on a document. Toolkit to easily incorporate celebratory splashes in 'Rmarkdown' and 'shiny' apps.

The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.