A database containing the names 
  of the babies born in Quebec between 1980 and 2020.

Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.

Early generation breeding trials are to be conducted in multiple 
             environments where it may not be possible to replicate all the 
             lines in each environment due to scarcity of resources. For such 
             situations, partially replicated (p-Rep) designs have wide 
             application potential as only a proportion of the test lines are 
             replicated at each environment. A collection of several utility 
             functions related to p-Rep designs have been developed. Here, the 
             package contains six functions for a complete stepwise analytical 
             study of these designs. Five functions pRep1(), pRep2(), pRep3(), 
             pRep4() and pRep5(), are used to generate five new series of p-Rep 
             designs and also compute average variance factors and canonical 
             efficiency factors of generated designs. A fourth function NCEV() 
             is used to generate incidence matrix (N), information matrix (C), 
             canonical efficiency factor (E) and average variance factor (V). 
             This function is general in nature and can be used for studying the 
             characterization properties of any block design. A construction 
             procedure for p-Rep designs was given by Williams et al.(2011) 
             <doi:10.1002/bimj.201000102> which was tedious and time consuming. 
             Here, in this package, five different methods have been given to 
             generate p-Rep designs easily.