Functions which facilitate harmonization of data from multiple
    different datasets. Data harmonization involves taking data sources with
    differing values, creating coding instructions to create a harmonized
    set of values, then making those data modifications. 'psHarmonize' will
    assist with data modification once the harmonization instructions are
    written. Coding instructions are written by the user to create a
    "harmonization sheet". This sheet catalogs variable names, domains
    (e.g. clinical, behavioral, outcomes), provides R code instructions for
    mapping or conversion of data, specifies the variable name in the
    harmonized data set, and tracks notes. The package will then harmonize
    the source datasets according to the harmonization sheet to create a
    harmonized dataset. Once harmonization is finished, the package also has
    functions that will create descriptive statistics using 'RMarkdown'. Data
    Harmonization guidelines have been described by Fortier I, Raina P,
    Van den Heuvel ER, et al. (2017) <doi:10.1093/ije/dyw075>. Additional
    details of our R package have been described by Stephen JJ, Carolan P,
    Krefman AE, et al. (2024) <doi:10.1016/j.patter.2024.101003>.

In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.

Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics (PSID) delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about 
    the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.