R/lang_en_EN.R

In NicolasStefaniak/easieR: easieR: A GUI R metapackage.

load_en_EN <- function() {
  .dico <<- new.env(parent=emptyenv())
 assign("ask_chose_analysis", "Please choose the analysis you wish to perform.", envir = .dico)
assign("ask_mcnemar_repeated_measure", "McNemar test: the modalities are not the same for the McNemar test. Is it truly a repeated measures factor?", envir = .dico)
assign("ask_mediation_type", "What type of mediation?", envir = .dico)
assign("ask_mediator", "Please choose the mediator", envir = .dico)
assign("ask_minus_left_hand_variables", "Please select the variable(s) to the left of the *minus* symbol", envir = .dico)
assign("ask_minus_right_hand_variables", "Please select the variable(s) to the right of the *minus* symbol.", envir = .dico)
assign("ask_minus_right_operand_variable_or_value", "Are the values to the right of the *minus* symbol variables or a value?", envir = .dico)
assign("ask_missing_values_detected_what_to_do", "Missing values have been detected. How would you like to handle them?", envir = .dico)
assign("ask_missing_values_treatment", "Missing values treatment?", envir = .dico)
assign("ask_missing_values_value_na_on_empty", "If some data are missing, how are they defined? You may leave NA if the cells are empty", envir = .dico)
assign("ask_missing_value_treatment", "Number of missing values per variable. How would you like to handle them?", envir = .dico)
assign("ask_modalities_for_variable", "Which modalities would you like to select for the variable", envir = .dico)
assign("ask_modalities_to_keep", "Please select the modalities you wish to keep.", envir = .dico)
assign("ask_name_for_dataset", "What name do you want to assign to the data?", envir = .dico)
assign("ask_name_to_attribute_to", "What name do you want to assign to", envir = .dico)
assign("ask_nb_factors_repeated_measure", "How many repeated measures factors?", envir = .dico)
assign("ask_new_variable_name", "What name do you want to assign to the new variable?", envir = .dico)
assign("ask_norm_value", "What is the value of the norm?", envir = .dico)
assign("ask_not_enough_obs_verify_dataset", "There are not enough observations to perform the analysis. Please check your data and ensure that there are at least three observations per modality for each factor", envir = .dico)
assign("ask_null_hypothesis_tests_or_bayesian_factors", "Would you like null hypothesis tests and/or Bayesian factors?", envir = .dico)
assign("ask_numerator_variable_or_value", "Is the numerator a variable or a value?", envir = .dico)
assign("ask_numerator_variable", "Please select the variable for the numerator", envir = .dico)
assign("ask_obs_to_remove", "Which observation would you like to remove from the analyses? 0=none", envir = .dico)
assign("ask_other_options", "Other options?", envir = .dico)
assign("ask_ponderate_analysis_by_a_sample_var", "Should the analysis be weighted by an effective variable?", envir = .dico)
assign("ask_positive_val_variable_or_value", "Are the positive values variable(s) or a value?", envir = .dico)
 assign("ask_2x2_table" , " 2x2 table?" , envir=.dico)
  assign("ask_2x2_table_value" , "Please provide the value for tables 2x2" , envir=.dico)
  assign("ask_add_a_value_to_empty_cells" , "Does an empty cell value for polychoric correlations need to be added? To enter the values, choose TRUE, otherwise choose [default]" , envir=.dico)
  assign("ask_add_value_to_total" , "do you still want to add a total value?" , envir=.dico)
  assign("ask_analysis_by_group" , "Group analysis?" , envir=.dico)
  assign("ask_analysis_on_complete_data_or_remove_outliers" , "Do you want to perform analysis on the complete data or on the data for which the influential values have been excluded?" , envir=.dico)
  assign("ask_analysis_type" , "What analysis do you want to make?" , envir=.dico)
  assign("ask_are_frequences_free_parameters" , "is the frequency of the different groups a free parameter? " , envir=.dico)
  assign("ask_are_there_inversed_items" , "Are there any inverted items?" , envir=.dico)
  assign("ask_are_you_ready" , "are you ready?" , envir=.dico)
  assign("ask_baseline" , "What is the baseline?" , envir=.dico)
  assign("ask_bigger_tables_value" , "Please enter the value for tables larger than 2x2" , envir=.dico)
  assign("ask_bootstrap_number_min_500" , "Please enter the number of bootstraps. A minimum of 500 is ideally required. May take time for N > 1000" , envir=.dico)
  assign("ask_bootstrap_numbers_1_for_none" , "Please enter the number of bootstraps. To disable bootstrap, choose 1" , envir=.dico)
  assign("ask_bootstraps_number" , "Number of bootstraps?" , envir=.dico)
  assign("ask_cancel_entered_value_not_num" , "The value you entered is not numeric. Do you want to cancel this analysis?" , envir=.dico)
  assign("ask_cauchy_apriori_distribution" , "Please enter the Cauchy prior distribution " , envir=.dico)
  assign("ask_center" , "Center?" , envir=.dico)
  assign("ask_center_numeric_variables" , "Do you want to center the numerical variables? Centrering is generally advised (e.g., Schielzeth, 2010). " , envir=.dico)
  assign("ask_chi_squared_type" , "Please enter the type of chi-square test you want to perform." , envir=.dico)
  assign("ask_choose_a_variable_with_at_least_two_modalities" , "A categorical variable must have at least two different modalities. Please choose a variable with at least two modes" , envir=.dico)
  assign("ask_choose_analysis" , "Please choose the analysis you want to perform." , envir=.dico)
  assign("ask_chose_categorial_ranking_factor" , "Please select the categorical classification factor." , envir=.dico)
  assign("ask_chose_cols_corresponding_to_repeated_measures" , "Please choose all the columns corresponding to the modalities of the variables in repeated measures" , envir=.dico)
  assign("ask_chose_covariables" , "Please choose the covariates" , envir=.dico)
  assign("ask_chose_database" , "Please select the database" , envir=.dico)
  assign("ask_chose_defining_groups" , "Please select the group definition" , envir=.dico)
  assign("ask_chose_dependant_variable" , "Please select the dependent variable. " , envir=.dico)
  assign("ask_chose_first_judge" , "Please select the first judge" , envir=.dico)
  assign("ask_chose_independant_group_variables" , "Please choose the variables with independent groups" , envir=.dico)
  assign("ask_chose_interaction_model_predictors" , "Please choose the predictors to include into the interaction model. It is necessary to have at least two variables" , envir=.dico)
  assign("ask_chose_manifest_variables_at_least_three" , "Please select the observed variables you want to analyze. You must choose at least three variables" , envir=.dico)
  assign("ask_chose_ranking_categorial_factor" , "Please select the categorical classification factor." , envir=.dico)
  assign("ask_chose_rotation" , "Please choose the type of rotation. Oblique rotation is adapted in the human sciences" , envir=.dico)
  assign("ask_chose_sample_variables" , "Please select the variable(s) defining the sample" , envir=.dico)
  assign("ask_chose_second_judge" , "Please select the second judge" , envir=.dico)
  assign("ask_chose_selection_method" , "Please choose the selection method you wish to use" , envir=.dico)
  assign("ask_chose_the_working_dir" , "Please select working directory" , envir=.dico)
  assign("ask_chose_variables_at_least_five" , "Please select the variables you want to analyze. You must choose at least five variables" , envir=.dico)
  assign("ask_chose_variables_at_least_three" , "Please select the variables you want to analyze. You must choose at least three variables" , envir=.dico)
  assign("ask_chose_variable" , "Please choose the variables you want to analyze." , envir=.dico)
  assign("ask_chose_variable_x_axis" , "Please select the x-axis variable" , envir=.dico)
  assign("ask_chose_variable_y_axis" , "Please select the y-axis variable " , envir=.dico)
  assign("ask_coding_criterion" , "What coding criterion do you want?" , envir=.dico)
  assign("ask_col_separation_index" , "When saving your file, what is the column delimiter?" , envir=.dico)
  assign("ask_complete_or_outliers" , "Do you want to perform analyses on complete data or on data without influential values?" , envir=.dico)
  assign("ask_constant_parameters" , "Constant parameters?" , envir=.dico)
  assign("ask_continue" , "Continue?" , envir=.dico)
  assign("ask_contrast_must_respect_ortho" , "Contrasts must respect orthogonality. Do you want to continue?" , envir=.dico)
  assign("ask_control_variables" , "Please enter the variable(s) to control" , envir=.dico)
  assign("ask_convert_dependant_variable_to_dichotomic" , "do you want to convert the dependent variable into a dichotomous variable?" , envir=.dico)
  assign("ask_correction_desired" , "Please enter the type of probability correction you want to apply" , envir=.dico)
  assign("ask_correction_type" , "Type of correction?" , envir=.dico)
  assign("ask_correlated_or_orthogonal_factors" , "Are the factors correlated (FALSE) or orthogonal (TRUE)?" , envir=.dico)
  assign("ask_correlation_matrix_could_not_be_computed" , "The correlation matrix could not be computed. Do you want to try again?" , envir=.dico)
  assign("ask_correlation_type" , "Please choose the type of correlations you want to compute. Default for dichotomous variables are tetrachoric correlations." , envir=.dico)
  assign("ask_corr_or_partial_correlations" , " Correlations or partial correlations?" , envir=.dico)
  assign("ask_could_not_converge_model_verify_correlation_matrix" , "The model did not succeed to converge. Please check your correlation matrix and try again with different parameters" , envir=.dico)
  assign("ask_could_not_finish_analysis_respecify_parameters" , "We were unable to complete the analysis correctly. Please try to respecify the parameters" , envir=.dico)
  assign("ask_covariables" , "Covariate(s)?" , envir=.dico)
  assign("ask_criterion_for_dichotomy" , "Please state the criterion on which you want to dichotomize your variable. You can use the median or choose a specific threshold. " , envir=.dico)
  assign("ask_criterion_for_obs_to_keep" , "Please specify the criterion for the observations you want to keep." , envir=.dico)
  assign("ask_criterion_for_variable" , "What criterion do you want to use for the variable" , envir=.dico)
  assign("ask_data" , "Data?" , envir=.dico)
  assign("ask_data_format" , "What is the format of your data?" , envir=.dico)
  assign("ask_decimal_symbol" , "If some data contain decimals, what is the decimal symbol used?" , envir=.dico)
  assign("ask_denominator_variable_or_value" , "Is the denominator a variable or a value? " , envir=.dico)
  assign("ask_denominator_variable" , "Please select the variable for the denominator " , envir=.dico)
  assign("ask_dependant_variable_with_less_than_three_val_verify_dataset" , "The dependent variable has fewer than three different values. Check your dataset or consider whether the analysis is relevant." , envir=.dico)
  assign("ask_did_not_specify_nb_factors_repeated_measure_exit" , "You haven't specified the number of factors for repeated measures. Do you want to quit?" , envir=.dico)
  assign("ask_distribution" , "Distribution?" , envir=.dico)
  assign("ask_distribution_type" , "What distribution do you want?" , envir=.dico)
  assign("ask_empty_cells" , "Empty Cells?" , envir=.dico)
  assign("ask_enter_different_values" , "Please enter different values" , envir=.dico)
  assign("ask_enter_number_of_to_be_removed_variable" , "You must enter the number to identify which observation should be removed." , envir=.dico)
  assign("ask_exit_because_of_alpha_on_non_matrix" , "You are trying to compute an alpha on a non-matrix object. Do you want to exit this analysis?" , envir=.dico)
  assign("ask_exit_no_lower_bound_specified" , "You have not specified the lower bound. Do you want to exit the selection?" , envir=.dico)
  assign("ask_exit_no_upper_bound_specified" , "You have not specified the upper bound. Do you want to exit the selection?" , envir=.dico)
  assign("ask_exportation_filename" , "What name do you want to assign to the exported file?" , envir=.dico)
  assign("ask_factorial_scores" , "factorial scores?" , envir=.dico)
  assign("ask_factors_number_for_hierarchical_structure" , "Please specify the number of factors in the hierarchical structure. " , envir=.dico)
  assign("ask_factors_ortho" , "Orthogonality of factors?" , envir=.dico)
  assign("ask_factors_superior_level" , "Number of factors at the higher level?" , envir=.dico)
  assign("ask_family" , "Please specify the family (i.e. type of distribution). " , envir=.dico)
  assign("ask_file_format" , "Which file format?" , envir=.dico)
  assign("ask_file_format_to_import" , "What format is your file saved?" , envir=.dico)
  assign("ask_first_categorical_set" , "Please select the first categorical factor(s) set" , envir=.dico)
  assign("ask_first_variables_set" , "Please select the first set of variables" , envir=.dico)
  assign("ask_fixed_covariables" , "fixed covariates?" , envir=.dico)
  assign("ask_freq_constance" , "Frequency constancy ?" , envir=.dico)
  assign("ask_f_value" , "What f-value do you want to use?" , envir=.dico)
  assign("ask_group_variable" , "Group variable?" , envir=.dico)
  assign("ask_headers_in_database" , "Is the name of the variables on the first line of your database? Choose TRUE if so" , envir=.dico)
  assign("ask_hierarchical_analysis" , "Do you want to perform a hierarchical analysis? " , envir=.dico)
  assign("ask_how_many_modalities" , "How many modalities" , envir=.dico)
  assign("ask_how_standard_error_must_be_estimated" , "How should the standard error be estimated?" , envir=.dico)
  assign("ask_how_to_remove" , "How do you want to remove them?" , envir=.dico)
  assign("ask_how_to_treat_missing_values" , "Missing values were detected. How do you want to treat them? Keeping all observations can bias the results. " , envir=.dico)
  assign("ask_id_variable" , "Please select the variable identifying the participants" , envir=.dico)
  assign("ask_imitate" , "Impute?" , envir=.dico)
  assign("ask_independant_variable" , "Please select the independent variable. " , envir=.dico)
  assign("ask_information_matrix" , "Information Matrix?" , envir=.dico)
  assign("ask_integrate_factorial_scores_in_data" , "Do you want factorial scores to be included in your data?" , envir=.dico)
  assign("ask_inversed_items" , "reversed items?" , envir=.dico)
  assign("ask_is_model_correct" , "Is your model correct?" , envir=.dico)
  assign("ask_latent_variables_number" , "Please specify the number of latent variables" , envir=.dico)
  assign("ask_level" , "Please select the level" , envir=.dico)
  assign("ask_likelihood" , " Likelihood?" , envir=.dico)
  assign("ask_linebase_modalities" , "Please specify the modality(ies) that will be used for the base line (e.g. 0). The other modes will be grouped in category 1." , envir=.dico)
  assign("ask_log_base" , "Please specify the base of the logarithm.To get e, type e" , envir=.dico)
  assign("ask_lower_bound" , "Lower limit?" , envir=.dico)
  assign("ask_predictor" , "please specify the predictor" , envir=.dico)
  assign("ask_press_enter_to_continue" , "Support [enter] to continue" , envir=.dico)
  assign("ask_probabilities_for_modalities" , "Please enter the probabilities corresponding to each modality of the variable. " , envir=.dico)
  assign("ask_probabilities" , "Probabilities?" , envir=.dico)
  assign("ask_probability_value" , "What probability value do you want to use?" , envir=.dico)
  assign("ask_redefine_analysis_because_modalities_product_is_superior_to_obs" , "The product of the modalities of the variables defining the groups is superior to your observations. You need at least one observation by combination of madalities of your variables. Please redefine your analysis" , envir=.dico)
  assign("ask_regroup_modalities" , "Do you want to group the modalities?" , envir=.dico)
  assign("ask_rename_variables_with_special_char" , "Some variable names contain special characters that can create bugs. Do you want to rename these variables?" , envir=.dico)
  assign("ask_results_desired" , "What results do you want?" , envir=.dico)
  assign("ask_results_output" , "Output?" , envir=.dico)
  assign("ask_sampling_type" , "What type of sampling have you done for your analysis?" , envir=.dico)
  assign("ask_save_results_in_external_file" , "Do you want to save results to an external file?" , envir=.dico)
  assign("ask_second_categorical_set" , "Please select the second categorical factor(s) set" , envir=.dico)
  assign("ask_second_mediator" , "Please specify the second mediator. " , envir=.dico)
  assign("ask_second_variables_set" , "Please select the second set of variables" , envir=.dico)
  assign("ask_selection_method" , "What method should be used for the selection?" , envir=.dico)
  assign("ask_select_variables_or_modalities_of_repeated_measure_variable" , "Please select the variables OR modalities of the variables a measure(s). " , envir=.dico)
  assign("ask_separation_value" , "Please specify the threshold value" , envir=.dico)
  assign("ask_shorten_long_variables_names" , "Some variables have particularly long names that can generate playback. Do you want to shorten them?" , envir=.dico)
  assign("ask_should_intercept_of_latent_variable_be_fixed_to_zero" , "Should the intercept of latent variables have to be fixed to 0?" , envir=.dico)
  assign("ask_should_intercept_of_obs_variables_be_fixed_to_zero" , "Should the intercept of observed variables have to be fixed to 0?" , envir=.dico)
  assign("ask_simple_or_partial_corr" , "Simple or partial correlations?" , envir=.dico)
  assign("ask_specify_all_parameters_or_imitate_specific_software" , "Do you want to specify all the parameters [default] or imitate any particular software?" , envir=.dico)
  assign("ask_specify_datasheet_to_import" , "Please specify the worksheet you want to import" , envir=.dico)
  assign("ask_specify_groups" , "Specify groups?" , envir=.dico)
  assign("ask_specify_inverted_item" , "Please specify the inverted items" , envir=.dico)
  assign("ask_specify_likelihood" , "Please specify the likelihood. " , envir=.dico)
  assign("ask_specify_norm_value" , "Please specify the norm value " , envir=.dico)
  assign("ask_specify_other_options" , "Specify other options?" , envir=.dico)
  assign("ask_specify_sample" , "Specify the actual sample?" , envir=.dico)
  assign("ask_specify_sample_variable" , "Specify the actual sample size variable?" , envir=.dico)
  assign("ask_specify_variables_for_ranks" , "Please specify the variables to be used for the row ranking" , envir=.dico)
  assign("ask_specify_variables_type" , "Please specify the type(s) of variable(s) you want to include in the analysis. You can choose several (e.g., for mixed anova or ancova)" , envir=.dico)
  assign("ask_standard_error" , "Standard Error?" , envir=.dico)
  assign("ask_standardization" , "Standardization?" , envir=.dico)
  assign("ask_standardization_vl" , "Standardization of latent variables?" , envir=.dico)
  assign("ask_standardize_obs_variables_before" , "Do you want to standardize (i.e. centrer and scale) the variables observed beforehand (TRUE) or not (FALSE)?" , envir=.dico)
  assign("ask_statistical_approach" , "Which Statistical approach?" , envir=.dico)
  assign("ask_subgroups" , "You can compute descriptive statistics by sub-group using one or more categorical variables. Do you want to specify the subgroups?" , envir=.dico)
  assign("ask_sufficient_matrix_for_afe" , "Is the matrix suitable for an EFA?" , envir=.dico)
  assign("ask_suppress_this_obs" , "Do you want to remove this observation?" , envir=.dico)
  assign("ask_test_hierarchical_structure" , " Do you want to test a hierarchical structure? Omega tests a hierarchical structure and a hierarchical AFE will be performed." , envir=.dico)
  assign("ask_time1" , "Please select time 1." , envir=.dico)
  assign("ask_time2" , "Please select time 2." , envir=.dico)
  assign("ask_transform_numerical_to_categorial_variables" , "You must use categorical variables. Do you want to convert numerical variables into categorical ones?" , envir=.dico)
  assign("ask_troncature_threshold" , "Please set the troncation threshold " , envir=.dico)
  assign("ask_t_test_type" , "Please specify the type of t-test you want to perform." , envir=.dico)
  assign("ask_type_correlation" , "Please specify the type of correlation you want to compute." , envir=.dico)
  assign("ask_upper_bound" , "Upper bound?" , envir=.dico)
  assign("ask_value_for_missing_values" , "What value defined the missing data?" , envir=.dico)
  assign("ask_value_for_operation" , "Please specify the value to use in your mathematical operation. " , envir=.dico)
  assign("ask_value_for_selected_obs" , "Please specify the value to select observations by. " , envir=.dico)
  assign("ask_value" , "Retrieve value?" , envir=.dico)
  assign("ask_variabels_for_polyc_tetra_mixt_corr" , "Please select the variables for polychoric/tetrachoric/mixte correlations." , envir=.dico)
  assign("ask_variable_at_this_point" , "What variable is at this step" , envir=.dico)
  assign("ask_variable_name" , "Name of new variable?" , envir=.dico)
  assign("ask_variables_for_description_statistics" , "Please select the variables for which you want descriptive statistics" , envir=.dico)
  assign("ask_variables_groups" , "groupes of variables?" , envir=.dico)
  assign("ask_variables_names" , "Names of the  variables?" , envir=.dico)
  assign("ask_variables_to_abs" , "Please select the variables to apply absolute value to " , envir=.dico)
  assign("ask_variables_to_add" , "Please select the variables to add." , envir=.dico)
  assign("ask_variables_to_exp" , "Please select the variables to apply the exponent to" , envir=.dico)
  assign("ask_variables_to_log" , "Please select the variables for which logarithm transformation is required" , envir=.dico)
  assign("ask_variables_to_mean" , "Please select the variables to average " , envir=.dico)
  assign("ask_variables_to_multiply" , "Please select the variables to multiply. " , envir=.dico)
  assign("ask_variables_to_order" , "Please select the variable(s) to sort" , envir=.dico)
  assign("ask_variables_type_correlations" , "Please specify the type of variables. Tetra/polychoric correlations will be used for dichotomous or ordinal variables and Bravais-Pearson for continuous variables" , envir=.dico)
  assign("ask_variables_types_correlations" , "Please specify the type of variables. Tetra/polychoric correlations will be used for ordinal and Bravai-Pearson variables for continuous variables" , envir=.dico)
  assign("ask_variables_used_for_exponential" , "Please select the variables used in the exponential operation " , envir=.dico)
  assign("ask_variables_used_for_groups" , "Please select the variable(s) defining groups" , envir=.dico)
  assign("ask_variable" , "Variable to analyze?" , envir=.dico)
  assign("ask_wanted_model" , "Please choose the model you want to analyze with aov.plus" , envir=.dico)
  assign("ask_what_do_you_want" , "What do you want to do?" , envir=.dico)
  assign("ask_what_is_your_choice" , "What is your choice?" , envir=.dico)
  assign("ask_what_to_print" , "What do you want to display?" , envir=.dico)
  assign("ask_which_algorithm" , "Which algorithm do you want to use?" , envir=.dico)
  assign("ask_which_analysis_you_looking_for" , "What analysis are you looking for?" , envir=.dico)
  assign("ask_which_baseline" , "What is the baseline/reference level?" , envir=.dico)
  assign("ask_which_constant_parameters" , "What parameters do you want to keep constant?" , envir=.dico)
  assign("ask_which_contrasts_for_variable" , "What contrasts for the variable" , envir=.dico)
  assign("ask_which_contrasts" , "What kind of contrast do you want?" , envir=.dico)
  assign("ask_which_correction" , "Which probability correction do you want to apply? To apply no correction, choose +none+" , envir=.dico)
  assign("ask_which_data_to_analyse" , "What data do you want to analyze?" , envir=.dico)
  assign("ask_which_data_to_export" , "What data do you want to export?" , envir=.dico)
  assign("ask_which_estimator" , "Which estimator?" , envir=.dico)
  assign("ask_which_factors_combination_for_adjust_means" , "Which combination of factors do you want to use to display the adjusted means?" , envir=.dico)
  assign("ask_which_information_matrix_for_standard_error_estimation" , "On which information matrix should the estimation of standard errors be based?" , envir=.dico)
  assign("ask_which_mathematical_operation" , "Please choose the mathematical operation you want to perform" , envir=.dico)
  assign("ask_which_operation" , "Which operation do you want?" , envir=.dico)
  assign("ask_which_options" , "Which options?" , envir=.dico)
  assign("ask_which_options_to_specify" , "What options do you want to specify?" , envir=.dico)
  assign("ask_which_output" , "What output format do you want?" , envir=.dico)
  assign("ask_which_output_results" , "What results do you want?" , envir=.dico)
  assign("ask_which_regression_type" , "What type of regression?" , envir=.dico)
  assign("ask_which_results_warning_on_default_output" , "What results do you want? Warning: default outputs cannot be saved. If you want a savable output, choose ‘detail" , envir=.dico)
  assign("ask_which_rotation" , "What rotation" , envir=.dico)
  assign("ask_which_saturation_criterion" , "What  saturation criterion do you want to use?" , envir=.dico)
  assign("ask_which_size_effect" , "What effect size do you want?" , envir=.dico)
  assign("ask_which_squared_sum" , "What sum of squares do you want to use?" , envir=.dico)
  assign("ask_which_test" , "What test do you want to use?" , envir=.dico)
  assign("ask_which_value_for_operation" , "What value do you want for your mathematical operation?" , envir=.dico)
  assign("ask_which_variable_identifies_participants" , "What is the variable that identifies participants?" , envir=.dico)
  assign("ask_you_did_not_chose_a_variable_continue_or_abort" , "You have not chosen a variable. Do you want to continue (ok) or abort (cancel) this analysis?" , envir=.dico)
  assign("desc_abs_val_applied_to_var" , "the absolute value has been applied to the variable" , envir=.dico)
  assign("desc_accepted_values_are_z_and_grubbs" , "The values accepted for criteria are z and Grubbs " , envir=.dico)
  assign("desc_all_tests_description" , "The parametric model returns the classic anova, the nonparametric model performs the Kruskal Wallis test if it is a model with independent groups, or a Friedman anova for repeated measures. The Bayesian model is equivalent to the classic anova test but adopts a Bayesian approach, robust statistics are anovas on medians or truncated means, with or without bootstrap." , envir=.dico)
  assign("desc_alpha_increased_with_value_equals_to" , "you are increasing the type 1 error rate. The risk of making a type 1 error is" , envir=.dico)
  assign("desc_analysis_aborted" , "The analysis could not be completed" , envir=.dico)
  assign("desc_and" , "and" , envir=.dico)
  assign("desc_and_variabe" , "and variable" , envir=.dico)
  assign("desc_and_variable_y" , " and variable " , envir=.dico)
  assign("desc_applied_correction_is" , "the correction applied is the correction for" , envir=.dico)
  assign("desc_at_least_10_obs_needed" , " at least 10 observations plus the number of variables are needed to perform the analysis. Check your data." , envir=.dico)
  assign("desc_at_least_independant_variables_or_repeated_measures" , "It is essential to have at least independent groups variables or repeted measures" , envir=.dico)
  assign("desc_at_least_on_contrast_matrix_incorrect" , "At least one of your contrast matrices is not correct." , envir=.dico)
  assign("desc_at_least_one_denom_is_zero" , "At least one of the values in the denominator is a 0. The returned value in this case is infinite - inf" , envir=.dico)
  assign("desc_at_least_one_non_numeric" , "at least one variable is not numeric" , envir=.dico)
  assign("desc_at_least_one_var_is_not_num" , "at least one of the variables is not numerical" , envir=.dico)
  assign("desc_authorized_values_for_contrasts" , "The allowed values for contrasts are +none+ for no contrast, +pairwise+ for pairwise comparisons or a list of contrast coefficients" , envir=.dico)
  assign("desc_avoid_spaces_and_punctuations" , "Avoid spaces and punctuation signs, except . and _ " , envir=.dico)
  assign("desc_bayesian_factors_could_not_be_computed" , "Bayesian factors could not be computed. " , envir=.dico)
  assign("desc_beyond_with_lower_and_upper" , "Beyond (with a lower and higher limit)" , envir=.dico)
  assign("desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance" , "there are fewer than 3 observations for one of the groups or the variance of at least one group is 0. Results are likely to be significantly biased" , envir=.dico)
  assign("desc_bootstraps_number_must_be_positive" , "The number of bootstraps must be a positive integer" , envir=.dico)
  assign("desc_bootstrap_t_adapt_to_truncated_mean" , "The bootstrap-t method is adapted to the calculation of the truncated mean" , envir=.dico)
  assign("desc_cannot_compute_mahalanobis" , "Sorry, we cannot compute the Mahalanobis distance of your data. The analyses will be carried out on the complete data" , envir=.dico)
  assign("desc_cannot_group_variables_because_not_described" , "You cannot define a *groups* variable unless all variables have been descripted" , envir=.dico)
  assign("desc_cannot_have_both_within_RML_arguments" , "You cannot specify both within and RML arguments at the same time " , envir=.dico)
  assign("desc_cells_for_mcnemar" , "The cells used to calculate the McNemar test are :  1st row 2nd column and the 2nd row ,1st column" , envir=.dico)
  assign("desc_centered_data_schielzeth_recommandations" , "In accordance with Schielzeth 2010 recommendations, the data were centered beforehand" , envir=.dico)
  assign("desc_chi_squared_adjustment_on_variable_x" , "chi-squared adjustment on variable" , envir=.dico)
  assign("desc_close_browser_to_come_back" , "Do not forget to close the htmlt window (firexfox, chrome, internet explorer...) to return to the R session" , envir=.dico)
  assign("desc_cross_validation_is_not_yet_supported" , "Cross validation is not yet supported. " , envir=.dico)
  assign("desc_data_saved_in" , "data are saved in" , envir=.dico)
  assign("desc_data_succesfully_ordered" , "data have been successfully sorted " , envir=.dico)
  assign("desc_descriptive_statistics_on" , "Descriptive statistics on" , envir=.dico)
  assign("desc_distribution_is_hypergeometric_when" , "The option *Total fixed effect for rows and columns*applies when rows and columns totals are fixed. The distribution is hypergeometric" , envir=.dico)
  assign("desc_each_participant_must_appear_only_once_" , "Each participant must appear once and only once for each combination of modes" , envir=.dico)
  assign("desc_effect_size" , "Effect size" , envir=.dico)
  assign("desc_effect_size_by_walker" , "The effect size is calculated using the formula proposed by Walker, 2003" , envir=.dico)
  assign("desc_entered_value_not_num" , "the value entered is not numerical" , envir=.dico)
  assign("desc_exponential_has_been_applied_to_var" , "an exponential transformation has been applied to the variable" , envir=.dico)
  assign("desc_facotrs_must_be_positive_int_inferior_to_variables_num" , "The number of factors must be a positive integer less than the number of variables" , envir=.dico)
  assign("desc_fb_ratio_between_models" , "FB ratio between models" , envir=.dico)
  assign("desc_file_is_saved_in" , "the file is saved in" , envir=.dico)
  assign("desc_flattening_and_asymetry_configurable" , "You can specify truncation and parameters for flattening and asymetry by choosing other options" , envir=.dico)
  assign("desc_for_bigger_samples_bootstrap_t_prefered" , "For larger samples, the boostrap using the t method is preferred." , envir=.dico)
  assign("desc_for_easier_to_work" , "In order for easieR to work properly, Pandoc must be installed from the following URL: https://github.com/jgm/pandoc/releases" , envir=.dico)
  assign("desc_graph_thickness_gives_density" , "The thickness of the graph represents the density, allowing for a better view of the distribution. " , envir=.dico)
  assign("desc_has_been_added_to" , "has been added to" , envir=.dico)
  assign("desc_has_been_added_to_variable" , " has been added to variable" , envir=.dico)
  assign("desc_has_been_applied_to_variable" , " has been applied to the variable" , envir=.dico)
  assign("desc_has_been_put_to_the_power_of" , " has been raised to the power of" , envir=.dico)
  assign("desc_has_multiplied_variables" , "has multiplies the variables" , envir=.dico)
  assign("desc_highest_value" , "Highest Value" , envir=.dico)
  assign("desc_how_to_cite_easier" , "To quote easieR in your publication use:  Stefaniak, N. (2020). " , envir=.dico)
  assign("desc_identical_option_total_sample" , "The total fixed staffing option for columns is identical to the previous option for columns" , envir=.dico)
  assign("desc_identified_outliers" , "Observations identified as influential" , envir=.dico)
  assign("desc_if_true_covariates_as_fixed" , "If true, exogenous covariates are considered fixed, otherwise they are considered random. and their parameters are free" , envir=.dico)
  assign("desc_if_true_latent_residuals_one" , "If true, the residuals of latent variables are fixed to 1, otherwise the parameters of the latent variable are estimated by fixing the first indicator to 1" , envir=.dico)
  assign("desc_improve_likelihood_for_each_variable" , "Improving likelihood for each variable" , envir=.dico)
  assign("desc_incorrect_model" , "The specified model is incorrect. Check your variables and model" , envir=.dico)
  assign("desc_instable_model_high_multicolinearity" , "Multicolinearity is too high. The model is unstable" , envir=.dico)
  assign("desc_insufficient_obs" , "The number of observations is insufficient to complete the analyses for this group" , envir=.dico)
  assign("desc_insufficient_sample_for_combinations_between" , "The number of combinations between the variable is insufficient " , envir=.dico)
  assign("desc_in_that_case_non_parametric_is_classical_chi_squared" , "In this case, the nonparametric test is the classic chi-square test" , envir=.dico)
  assign("desc_issue_in_hierarchical_regression" , "A problem has been identified in the steps of your hierarchical regression" , envir=.dico)
  assign("desc_kmo_could_not_be_computed_verify_matrix" , "The KMO could not be calculated. Check your correlation matrix." , envir=.dico)
  assign("desc_kmo_must_strictly_be_more_than_a_half" , "the KMO must be strictly greater than 0.5" , envir=.dico)
  assign("desc_kmo_on_matrix_could_not_be_obtained" , "The KMO for the matrix could not be obtained." , envir=.dico)
  assign("desc_kmo_on_matrix_could_not_be_obtained_trying" , "The KMO for the matrix could not be obtained. Attempting to smooth the correlation matrix" , envir=.dico)
  assign("desc_large_format_must_be_numeric_or_integer" , "If your data is in wide format, all measurements must be numeric or integer" , envir=.dico)
  assign("desc_list_of_objects_still_in_mem" , "List of objects still in R memory " , envir=.dico)
  assign("desc_log_with_base" , " logarithm with base" , envir=.dico)
  assign("desc_manifest_variables_of" , "Manifest Variables of" , envir=.dico)
  assign("desc_manual_contrast_need_coeff_matrice" , "If you enter contrasts manually, each variables in the analysis must have a corresponding coefficient matrix" , envir=.dico)
  assign("desc_matrix_is_singular_mardia_cannot_be_performed" , "The matrix is singular and the Marida test cannot be performed. Only univariate analyses can be conducted" , envir=.dico)
  assign("desc_mcnemar_need_2x2_table_yours_are_different" , "The McNemar test requieres a 2x2 table. The dimensions of your table are different. " , envir=.dico)
  assign("desc_modalities_product_must_correspond_to_cols_selected" , "the product of the modalities of each variable must match the number of selected columns. " , envir=.dico)
  assign("desc_model_contains_error" , "The model cannot be evaluated. It likely contain an error." , envir=.dico)
  assign("desc_model_could_not_converge" , "The model failed to converge. The parameters were ajusted to help convergence" , envir=.dico)
  assign("desc_model_seems_incorrect_could_not_be_created" , "The model appears to be incorrect and could not be created." , envir=.dico)
  assign("desc_most_common_effect_size" , "the most commonly used effect size is partial eta square (pes). The most precise one is generalized eta squared (ges)" , envir=.dico)
  assign("desc_multicolinearity_risk" , " risk of multicolinearity if the matrix determinant is less than 0.00001" , envir=.dico)
  assign("desc_multiple_ways_to_compute_squares_sum" , "There are several ways to compute the sum of squares. Most commercial software defaults to type 3 sums, which prioritize interactions over main effects. " , envir=.dico)
  assign("desc_must_be_dichotomic" , ". The variable must be dichotomous. Current modalites are incompatible with logistic regression." , envir=.dico)
  assign("desc_nb_factors_must_be_positive_integer" , "The number of factors must be a positive integer less than the number of variables" , envir=.dico)
  assign("desc_need_at_least_three_observation_by_combination" , "Some combinations of modalities have fewer than 3 observations. At least 3 observations are required per combination" , envir=.dico)
  assign("desc_neg_log_impossible" , "it is not possible to calculate logarithms with a negative base. NA is fired" , envir=.dico)
  assign("desc_no_analysis_can_be_performed_given_your_data" , "The variables you selected do not allow any analysis to be performed. Please redefine your analysis" , envir=.dico)
  assign("desc_no_data_in_R_memory" , " no data found in the memory of R, please import the dataset to perform the analysis" , envir=.dico)
  assign("desc_non_equal_independant_variable_modalities_occurrence" , "The number of occurrences for each modality of your independent variable is not equal. Please select a participant identifier" , envir=.dico)
  assign("desc_non_numeric_value" , "The input value is not numeric, please enter a numeric value" , envir=.dico)
  assign("desc_non_numeric_variable" , "the variable is not numeric" , envir=.dico)
  assign("desc_non_param_are_rho_and_tau" , "The nonparametric tests used are Spearman rho and Kendall tau" , envir=.dico)
  assign("desc_non_param_is_wilcoxon_or_mann_withney" , "The non-parametric test is the Wilcoxon test (or Mann-Whitney)" , envir=.dico)
  assign("desc_no_obs_for_combination" , "no observations available for the combination" , envir=.dico)
  assign("desc_no_result_saved" , "no result has been saved" , envir=.dico)
  assign("desc_norm_must_be_numeric" , "The norm must be a numeric value. " , envir=.dico)
  assign("desc_no_saved_analysis_found" , "No saved analysis could be found" , envir=.dico)
  assign("desc_number_of_judge_is" , "the number of judges =" , envir=.dico)
  assign("desc_number_of_missing_values" , "Number of missing values per variable" , envir=.dico)
  assign("desc_number_of_observations_is" , "number of observations =" , envir=.dico)
  assign("desc_number_outliers_removed" , "Number of observations removed" , envir=.dico)
  assign("desc_obs_with_asterisk_are_outliers" , " observations marked with an asterisk are considered influential according to at least on a criterion" , envir=.dico)
  assign("desc_odd_ratio_cannot_be_computed" , "The odds ratio cannot be calculated for tables larger than 2x3 or tables containing zeros" , envir=.dico)
  assign("desc_only_one_dependant_variable_alllowed" , " only one dependent variable is allowed. " , envir=.dico)
  assign("desc_only_one_file_format_at_time_EPS_JPG" , "Only one file can be used at a time to save the figure (you have both EPS and JPG specified). " , envir=.dico)
  assign("desc_only_one_file_format_at_time_EPS_PDF" , " Only one file can be used at a time to save the figure (you have both PDF and EPS specified). " , envir=.dico)
  assign("desc_only_one_file_format_at_time_PDF_JPG" , " Only one file can be used at a time to save the figure (you have both PDF and JPG specified). " , envir=.dico)
  assign("desc_only_values_above_diagonal_are_adjusted_for_multiple_comp" , "Only values above the diagonal are adjusted for multiple comparisons" , envir=.dico)
  assign("desc_operation_succesful" , "The mathematic operation was successful." , envir=.dico)
  assign("desc_order" , "sort" , envir=.dico)
  assign("desc_outliers_identified_on_4_div_n" , "Influential values are identified based on 4/n criterion" , envir=.dico)
  assign("desc_outliers_identified_on_mahalanobis" , "Influential values are identified based on the Mahalanobis distance with a chi-squared threshold at 0.001" , envir=.dico)
  assign("desc_outliers_on_4_div_n" , "Influential values are identified based on the of 4/n criterion" , envir=.dico)
  assign("desc_packages_used_for_this_function" , "Packages used by this function" , envir=.dico)
  assign("desc_param_is_BP" , "The parametric test is the Bravais-Pearson correlation" , envir=.dico)
  assign("desc_param_is_t_test" , "The parametric test is the classical t-test" , envir=.dico)
  assign("desc_param_test_is_classical_reg_robusts_are_m_estimator" , "The parametric test is classical regression and robust tests are an based on M estimater and bootstrapping." , envir=.dico)
  assign("desc_percentile_bootstrap_prefered_for_small_samples" , "the percentile bootstrap method is preferred for small samples" , envir=.dico)
  assign("desc_perfectly_correlated_variables_in_matrix_trying_to_solve" , "you are trying to create a correlation matrix with perfectly correlated variables. This causes issues for Mahalanobis' distance calculation. We are attempting to resolve the problem." , envir=.dico)
  assign("desc_polyc_correlations_failed_rho_used_instead" , "Polychoric correlations failed. Spearman rho was used instead" , envir=.dico)
  assign("desc_proba_and_IC_estimated_on_bootstrap" , "Probabilities and confidence intervals are estimated using bootsrapping. The CI is corrected for multiple comparisons, unlike the reported probability." , envir=.dico)
  assign("desc_probabilities_vector_please_no_fraction" , "Probability vector. Note: please do not enter fractions" , envir=.dico)
  assign("desc_red_dot_is_mean_error_is_sd" , "The red dot represents the mean. The error bar represents the standard deviation" , envir=.dico)
  assign("desc_references" , "References for packages used for this analysis" , envir=.dico)
  assign("desc_removed_variable" , "deleted variable" , envir=.dico)
  assign("desc_removing_outliers_weakens_sample_size" , "Removing influential values results in too few modalites to complete the analysis" , envir=.dico)
  assign("desc_result_succesfully_imported_in" , "Results were successfully imported into" , envir=.dico)
  assign("desc_robusts_statistics_could_not_be_computed" , " robust statistics could not be computed" , envir=.dico)
  assign("desc_robust_statistics_are_alternative_to_the_principal_but_slower" , " robust statistics are an alternative to the main analysis, usually involving bootstrapping. These methods are generally slower" , envir=.dico)
  assign("desc_saturation_criterion_must_be_between_zero_and_one" , "The saturation criterion must be between 0 and 1." , envir=.dico)
  assign("desc_search_here" , "Type your search here" , envir=.dico)
  assign("desc_selected_obs_are_in" , "the observations you selected are in" , envir=.dico)
  assign("desc_selection_for_bayesian_factor_does_not_apply_to_complex_models" , " selection methods for Bayesian factors do not apply to complex models. " , envir=.dico)
  assign("desc_should_specify_nb_factors_repeated_measure" , "you must specify the number of repeated-maesures factors" , envir=.dico)
  assign("desc_single_dependant_variable_allowed_in_paired_t" , "There can be only one dependent variable for students-t tes for matched samples" , envir=.dico)
  assign("desc_singular_matrix_mahalanobis_on_max_info" , "Your matrix is singular, which is a concern. We are trying to solve the problem. If possible, the distance from Mahalanobis will then be calculated using the maximum information while avoiding singularity. " , envir=.dico)
  assign("desc_some_values_are_not_numeric" , "Not all entered values are numeric. Please enter numeric values only" , envir=.dico)
  assign("desc_special_characters_have_been_removed" , "Special accents/characters have been deliberately removed to ensure the portability of easieR across all computers. " , envir=.dico)
  assign("desc_specify_f_value" , "You must specify the value of the F. This value must be greater than 1" , envir=.dico)
  assign("desc_specify_lower_bound" , "you must specify the lower bound" , envir=.dico)
  assign("desc_specify_probability_value" , "You must specify the probability value. This value must be between 0 and 1" , envir=.dico)
  assign("desc_specify_upper_bound" , "you must specify the upper bound" , envir=.dico)
  assign("desc_standardized_saturation_on_correlation_matrix" , "standardized saturations based on the correlation matrix" , envir=.dico)
  assign("desc_succesfully_imported" , "data were imported correctly" , envir=.dico)
  assign("desc_succesful_operation" , "The operation was completed successfully" , envir=.dico)
  assign("desc_tested_model_is" , "the model test is" , envir=.dico)
  assign("desc_there_is_no_rotation" , "there is no rotation" , envir=.dico)
  assign("desc_the_variable_lower" , "variable" , envir=.dico)
  assign("desc_the_variable_upper" , "The variable" , envir=.dico)
  assign("desc_this_analysis_will_not_be_performed" , ". This analysis will not be performed." , envir=.dico)
  assign("desc_this_index_is_prefered_for_most_cases" , " This index is recommended in most situations. The modified M-estimator must be preferred for N<20" , envir=.dico)
  assign("desc_this_is_large_format" , "this is the wide format" , envir=.dico)
  assign("desc_this_is_long_format" , "this is the long format" , envir=.dico)
  assign("desc_times_less" , "times fewer" , envir=.dico)
  assign("desc_times_more" , "times more" , envir=.dico)
  assign("desc_to_display_results_use_summary" , "To display the results, please use summary(model.cfa)" , envir=.dico)
  assign("desc_total_observations" , "total number of observations" , envir=.dico)
  assign("desc_truncature_on_m_estimator_adapts_to_sample" , "The truncation on the M-estimetor adapts to the characteristics of the sample. " , envir=.dico)
  assign("desc_two_cols_are_needed" , "For a wide-format repeated factor, at least two columns are needed" , envir=.dico)
  assign("desc_two_modalities_for_independante_categorial_variable" , "You must use an independent categorical variable with two modalities" , envir=.dico)
  assign("desc_unauthorized_char_replaced" , "Unauthorized characters were used for the name. These characters were replaced by dots" , envir=.dico)
  assign("desc_unavailable_distal_mediations" , "Distal mediations are not currently available / Distal mediations are not available for now" , envir=.dico)
  assign("desc_user_exited_aov_plus" , "you left aov.plus" , envir=.dico)
  assign("desc_value_must_be_between_zero_and_one" , "The value must be between 0 and 1" , envir=.dico)
  assign("desc_value_must_be_numeric" , "The value must be numeric and between the minimum and maximum of the dependent variable. " , envir=.dico)
  assign("desc_variable_added" , "Variable added" , envir=.dico)
  assign("desc_variable_must_be_numeric_and_of_non_null_variance" , "the variable must be numeric and have a non-zero variance. " , envir=.dico)
  assign("desc_variable_must_be_positive_int" , "the variable must be a positive integer" , envir=.dico)
  assign("desc_variables_are_in" , "selected variables are in" , envir=.dico)
  assign("desc_we_could_not_compute_anova_on_medians" , "Sorry, we could not compute the anova on the medians, possibly due to a large number of tries." , envir=.dico)
  assign("desc_we_could_not_compute_robust_anova" , " Sorry, we could not compute the robust anova. " , envir=.dico)
  assign("desc_working_dir_is_now" , "The working directory is now set to" , envir=.dico)
  assign("desc_you_can_chose_predefined_or_manual_contrasts" , "You can choose predefined contrasts or specify them manually. If so, please select the manual specification option" , envir=.dico)
  assign("desc_you_can_still_add" , "You can still add a specific value to the total. Leave 0 if you don't want to add anything" , envir=.dico)
  assign("desc_you_can_still_multiply" , "You can still multiply the total by a specific value. Leave 1 if you do not wish to apply any further multiplication" , envir=.dico)
  assign("desc_you_did_this_operation" , "you have performed the following operation:" , envir=.dico)
  assign("desc_you_exited_afe" , "you exited AFE" , envir=.dico)
  assign("desc_you_have_selected" , "you have selected" , envir=.dico)
  assign("desc_you_must_give_obs_number" , "You must provide the observation number" , envir=.dico)
  assign("desc_your_dependant_variable_has" , "Your dependent variable has" , envir=.dico)
  assign("desc_z_must_be_a_number" , "z must be a number" , envir=.dico)
  assign("desc_author" , "author: 'Generate automatically by easieR'" , envir=.dico)
  assign("desc_title" , "title: 'Results of your analyses'" , envir=.dico)
  assign("txt_absolute_value" , "absolute value" , envir=.dico)
  assign("txt_added_variables_graph" , "Added variable plot" , envir=.dico)
  assign("txt_additions" , "Additions" , envir=.dico)
  assign("txt_additive_effects" , "Additive effects" , envir=.dico)
  assign("txt_additive_model_variables" , "Variables of the additive model" , envir=.dico)
  assign("txt_add_of_cols" , "Add columns" , envir=.dico)
  assign("txt_add_of_specific_value" , "Addition of a specific value" , envir=.dico)
  assign("txt_adequation_adjustement_indexes" , "Fit and adjustment indices" , envir=.dico)
  assign("txt_adequation_measurement_of_matrix" , "Matrix adequacy measurement" , envir=.dico)
  assign("txt_adequation_measures" , "Fit measures" , envir=.dico)
  assign("txt_adequation_outside_diagonal" , "Fit based on values outside the diagonal" , envir=.dico)
  assign("txt_adjusted_data_loftus_masson" , "Adjusted data (Loftus & Masson, 1994)" , envir=.dico)
  assign("txt_adjusted_means_graph" , "Adjusted-Means Averages" , envir=.dico)
  assign("txt_adjusted_means" , "Adjusted Means " , envir=.dico)
  assign("txt_adjustement_measure" , "Adjustment measures" , envir=.dico)
  assign("txt_adjusted_p_dot_value" , "Adjusted p value" , envir=.dico)
  assign("txt_agreement" , "Agreement" , envir=.dico)
  assign("txt_aic_criterion" , "AIC - Akaike Information criterion" , envir=.dico)
  assign("txt_alpha_warning" , "Alpha warning" , envir=.dico)
  assign("txt_alternative" , "alternative" , envir=.dico)
  assign("txt_analysis_factor_component" , "factor and component analyses" , envir=.dico)
  assign("txt_analysis_on" , "analysis on" , envir=.dico)
  assign("txt_analysis_on_truncated_means" , "Analysis on truncated means" , envir=.dico)
  assign("txt_analysis_on_variable" , "Analysis on variable" , envir=.dico)
  assign("txt_analysis_premature_abortion" , "Premature termination of analysis" , envir=.dico)
  assign("txt_ancova_application_conditions" , "Ancova application conditions" , envir=.dico)
  assign("txt_and_the_number_of_obs" , "and the number of observations =" , envir=.dico)
  assign("txt_and_YZ" , "and YZ =" , envir=.dico)
  assign("txt_anova_ancova" , " analysis of variance and covariance " , envir=.dico)
  assign("txt_anova" , "Anova" , envir=.dico)
  assign("txt_anova_on" , "anova on" , envir=.dico)
  assign("txt_anova_on_modified_huber_estimator" , "Anova using Huber's Modified Localization Estimator" , envir=.dico)
  assign("txt_anova_on_truncated_means" , "Anova on truncated means" , envir=.dico)
  assign("txt_anova_with_welch_correction" , "Anova with Welch correction for heterogeneous variances" , envir=.dico)
  assign("txt_apparied_correlations" , " paired correlations " , envir=.dico)
  assign("txt_apriori" , "a priori" , envir=.dico)
  assign("txt_autocorrelation" , "Autocorrection" , envir=.dico)
  assign("txt_backward" , "Backward" , envir=.dico)
  assign("txt_backward_step_descending" , "Backward stepwise (descending)" , envir=.dico)
  assign("txt_barlett_test" , "Barlett Test" , envir=.dico)
  assign("txt_bayes_factor_10" , "Bayes Factor (10)" , envir=.dico)
  assign("txt_bayes_factor" , "BayesFactor" , envir=.dico)
  assign("txt_bayesian_approach_hierarchical_models" , "Bayesian approach to hierarchical models" , envir=.dico)
  assign("txt_bayesian_factor_by_group" , "Baysian factor by group" , envir=.dico)
  assign("txt_bayesian_factor" , "Baysian factor" , envir=.dico)
  assign("txt_bayesian_factor_of_model" , "Bayesian Factor of the Model" , envir=.dico)
  assign("txt_bayesian_factors_10" , "Bayesian Factor 10" , envir=.dico)
  assign("txt_bayesian_factors_compute_null_with_bayesian_approach" , "Bayesian factors: computes the equivalent of the null hypothesis test using a Bayesian approach. " , envir=.dico)
  assign("txt_bayesian_factors_for_BP" , "Bayesian factor for Bravais-Pearson correlation" , envir=.dico)
  assign("txt_bayesian_factors_for_spearman" , "Bayesian factors for Spearman correlation" , envir=.dico)
  assign("txt_bayesian_factors_sequential" , "Sequential Bayesian Factors" , envir=.dico)
  assign("txt_bca_bootstrap_on_m_estimator" , " BCa Bootstrap on the M-estimetor" , envir=.dico)
  assign("txt_beta_table" , "Beta table" , envir=.dico)
  assign("txt_between" , "between" , envir=.dico)
  assign("txt_bidirectionnal" , "Bidirectional" , envir=.dico)
  assign("txt_b_m_estimator" , "b (M estimator)" , envir=.dico)
  assign("txt_bootstrap_on_BP" , "Bootstrap on Bravais Pearson correlation" , envir=.dico)
  assign("txt_bootstrap_t_method" , "bootstrap-t method" , envir=.dico)
  assign("txt_bootstrap_t_method_on_truncated_means" , "Bootstrap-t method on truncated means" , envir=.dico)
  assign("txt_BP_correlation_by_group" , "Bravais-Pearson correlation by group " , envir=.dico)
  assign("txt_breusch_pagan_test" , "Test for Heteroscedasticity (Breusch-Pagan test)" , envir=.dico)
  assign("txt_cancel" , "cancel" , envir=.dico)
  assign("txt_cauchy_prior_width" , "Cauchy Prior With (r)" , envir=.dico)
  assign("txt_center_or_center_reduce" , "Center / standardize" , envir=.dico)
  assign("txt_center_reduce" , " standardize " , envir=.dico)
  assign("txt_ceres_graph_linearity" , "CERES Test of Linearity" , envir=.dico)
  assign("txt_chi_adjustement" , "Chi-square Adjustment" , envir=.dico)
  assign("txt_chi_independance" , " Chi-square Independence" , envir=.dico)
  assign("txt_chi_results_between_var_x" , " Chi-square  Results between variable" , envir=.dico)
  assign("txt_chi_squared" , "Chi-square " , envir=.dico)
  assign("txt_chi_squared_empirical" , " empirical Chi-square " , envir=.dico)
  assign("txt_chi_squared_likelihood_max" , " Chi-square from maximum likelihood" , envir=.dico)
  assign("txt_chi_squared_null_model" , "chi-square of the null model " , envir=.dico)
  assign("txt_chi_squared_type" , " Chi-square type" , envir=.dico)
  assign("txt_coeff_table" , " coefficients table " , envir=.dico)
  assign("txt_col_correspoding_to_variable" , "Columns corresponding to the variable" , envir=.dico)
  assign("txt_col_mean" , " column means " , envir=.dico)
  assign("txt_cols" , "columns" , envir=.dico)
  assign("txt_col_separator" , "Column Separator" , envir=.dico)
  assign("txt_cols_in_repeated_measure" , "Columns for Repeated Measures" , envir=.dico)
  assign("txt_cols_multiplication" , "column multiplication" , envir=.dico)
  assign("txt_comma" , "comma" , envir=.dico)
  assign("txt_compare_to_baseline" , "Comparison with baseline" , envir=.dico)
  assign("txt_compare_two_correlations" , "Comparison of two correlations" , envir=.dico)
  assign("txt_comparison_of_two_correlations" , "comparison of two correlations" , envir=.dico)
  assign("txt_comparison_on_truncated_means" , "Comparison based on truncated means" , envir=.dico)
  assign("txt_comparisons_XY" , "Comparison XY=" , envir=.dico)
  assign("txt_comparison_to_norm" , "Comparison to norm" , envir=.dico)
  assign("txt_comparison_two_by_two" , "Pairwise Comparison" , envir=.dico)
  assign("txt_compile_report" , "generate a report" , envir=.dico)
  assign("txt_complementary_results" , "Complementary results (e.g. interaction contrasts and adjusted averages)" , envir=.dico)
  assign("txt_complete_dataset" , "Complete dataset" , envir=.dico)
  assign("txt_complete_model" , "Complete Model" , envir=.dico)
  assign("txt_complexity" , "complexity" , envir=.dico)
  assign("txt_complex_model" , "complex model" , envir=.dico)
  assign("txt_confidance_threshold" , "Confidence threshold (1- alpha)" , envir=.dico)
  assign("txt_confidence_interval_estimated_by_bootstrap" , "Confidence Interval estimated via bootstrap" , envir=.dico)
  assign("txt_confidence_interval" , "Confidence Interval" , envir=.dico)
  assign("txt_confidence_interval_inferior_limit" , " Confidence Interval - Lower bound" , envir=.dico)
  assign("txt_confidence_interval_superior_limit" , "Confidence Interval - Upper bound" , envir=.dico)
  assign("txt_confidence_interval_of_saturations_on_bootstrap" , " Bootstrap-Based Confidence Interval for Loadings - may be biased in presence of Heyhood cases" , envir=.dico)
  assign("txt_confidence_interval_on_bootstrap" , " Confidence Interval Based on Bootstrap " , envir=.dico)
  assign("txt_confidence_interval_on_standard_error" , "Confidence interval based on standard error" , envir=.dico)
  assign("txt_confirmatory_factorial_analysis" , " confirmatory factor analysis" , envir=.dico)
  assign("txt_contrast" , "contrast" , envir=.dico)
  assign("txt_contrasts" , "contrasts" , envir=.dico)
  assign("txt_contrasts_for" , "Contrasts for" , envir=.dico)
  assign("txt_contrasts_table_imitating_commercial_softwares" , "Constrast Table emulating commercial software output" , envir=.dico)
  assign("txt_contrasts_table" , "Contrast table" , envir=.dico)
  assign("txt_control_variables" , "Control Variable-s" , envir=.dico)
  assign("txt_correction_for_polyc_corr_must_be_between_zero_and_one" , "The correction factor for polychoric correlations must be between 0 and 1." , envir=.dico)
  assign("txt_correlation_between_scores_and_factors" , "Correlations between scores and factors" , envir=.dico)
  assign("txt_correlation_between_var_x" , "Correlation between variable" , envir=.dico)
  assign("txt_correlation_is" , "correction is" , envir=.dico)
  assign("txt_correlation_matrix_determinant" , "Determinant of the correlation matrix" , envir=.dico)
  assign("txt_correlation_matrix_determinant_information" , " Determinant of the correlation matrix: information" , envir=.dico)
  assign("txt_correlations_between_factors" , "correlations between factors" , envir=.dico)
  assign("txt_correlations_comparison" , "comparison of correlations" , envir=.dico)
  assign("txt_correlations_matrix_afe" , "Correlation matrix used for AFE" , envir=.dico)
  assign("txt_covariance_matrix_adjusted" , "Adjusted covariance matrix" , envir=.dico)
  assign("txt_covariance_matrix_estimated" , "Estimated covariance matrix" , envir=.dico)
  assign("txt_cox_snell_r_2" , "Cox and Snell R^2" , envir=.dico)
  assign("txt_cronbach_alpha" , "Cronbach Alpha" , envir=.dico)
  assign("txt_cronbach_alpha_on_whole_scale" , "Cronbach Alpha for the entire scale" , envir=.dico)
  assign("txt_cross_validation" , "Cross Validation " , envir=.dico)
  assign("txt_csv_file" , "CSV file" , envir=.dico)
  assign("txt_cumulated_explaination_ratio" , "Cumulative explaned ratio" , envir=.dico)
  assign("txt_cumulated_explained_variance_ratio" , " Cumulative proportion of explained variance" , envir=.dico)
  assign("txt_dataframe_choice" , "Dataframe selection" , envir=.dico)
  assign("txt_data_import_export_save" , "Data - (Import, export, backup)" , envir=.dico)
  assign("txt_decimal_separator" , " Decimals Separator " , envir=.dico)
  assign("txt_default_outputs" , " default outputs" , envir=.dico)
  assign("txt_delete_observations_with_missing_values" , "Delete observations with missing values" , envir=.dico)
  assign("txt_denominator" , "Denominator" , envir=.dico)
  assign("txt_dependant_variables" , " dependent Variable(s)" , envir=.dico)
  assign("txt_dependant_variable" , "dependent variable" , envir=.dico)
  assign("txt_descriptive_statistics_by_group" , "Descriptive statistics by group" , envir=.dico)
  assign("txt_detailed_corr_analysis" , "detailed correlation Analysis (Bravais Pearson/Spearman/tau) for one or few correlations" , envir=.dico)
  assign("txt_deviation" , "Deviance" , envir=.dico)
  assign("txt_dichotomic_ordinal" , "dichotomics/ordinal" , envir=.dico)
  assign("txt_difference" , "Difference" , envir=.dico)
  assign("txt_distance_mediation_effect" , "Indirect mediation effect" , envir=.dico)
  assign("txt_distance_mediator" , " distance to Mediatior " , envir=.dico)
  assign("txt_do_nothing_keep_all_obs" , "Do nothing - Keep all observations" , envir=.dico)
  assign("txt_dot" , "dot" , envir=.dico)
  assign("txt_durbin_watson_test_autocorr" , "Durbin-Watson test - autocorrelations" , envir=.dico)
  assign("txt_dw_statistic" , "Durbin-Watson statistics" , envir=.dico)
  assign("txt_dynamic_crossed_table" , "Dynamic Cross Table" , envir=.dico)
  assign("txt_effect" , "Effect" , envir=.dico)
  assign("txt_equals_to" , "egal to" , envir=.dico)
  assign("txt_error" , "error" , envir=.dico)
  assign("txt_estimated_parameters_not_standardized" , "Unstandardized estimated Parameters" , envir=.dico)
  assign("txt_estimated_parameters" , "estimated parameters" , envir=.dico)
  assign("txt_estimated_parameters_standardized" , "Standardized estimated parameters" , envir=.dico)
  assign("txt_estimation" , "estimation" , envir=.dico)
  assign("txt_excel_file" , " Excel file" , envir=.dico)
  assign("txt_exogenous_fixed_variables" , " fixed exogenous Variables [fixed.x=default]" , envir=.dico)
  assign("txt_expected" , "expected" , envir=.dico)
  assign("txt_expected_sample" , "Expected sample" , envir=.dico)
  assign("txt_experimental_pan_between" , " experimental panel" , envir=.dico)
  assign("txt_explaination_ratio" , "Proportion of explanation variance" , envir=.dico)
  assign("txt_explained_variance_ratio" , "proportion of explained variance " , envir=.dico)
  assign("txt_explained_variance" , "Variance explained" , envir=.dico)
  assign("txt_exponant" , "exponent" , envir=.dico)
  assign("txt_exponant_or_root" , " exponent or root" , envir=.dico)
  assign("txt_exponential" , "exponential" , envir=.dico)
  assign("txt_export_data" , "export data" , envir=.dico)
  assign("txt_factorial_analysis" , "factorial analysis" , envir=.dico)
  assign("txt_factorial_analysis_using_fa_with_method" , "factorial analysis using the fa function of the psych package. Specify the method" , envir=.dico)
  assign("txt_factorial_exploratory_analysis" , "Exploratory factor analysis" , envir=.dico)
  assign("txt_factor_name" , "factor name" , envir=.dico)
  assign("txt_factors" , "factors. " , envir=.dico)
  assign("txt_factors_ortho" , "Orthogonality of factors [orthogonal=FALSE]" , envir=.dico)
  assign("txt_factors_to_keep_accord_to_parallel_analysis_is" , "the number of factors to keep according to the parallel analysis is" , envir=.dico)
  assign("txt_fiability_analysis" , "Analysis of reliability and agreement" , envir=.dico)
  assign("txt_fiability_by_removed_item" , "reliability by deleted item " , envir=.dico)
  assign("txt_for_a_detailed_results_description_distal" , "For a detailed results, see ?distal.med" , envir=.dico)
  assign("txt_for_a_detailed_results_description_mediation" , "For a detailed results, see ?mediation" , envir=.dico)
  assign("txt_forward_step_ascending" , "Forward stepwise – non-decreasing" , envir=.dico)
  assign("txt_friedman_anova_pairwise_comparison" , "pairwise Comparison for Friedman's ANOVA" , envir=.dico)
  assign("txt_f_value" , "F value" , envir=.dico)
  assign("txt_get_working_dir" , "get working directory" , envir=.dico)
  assign("txt_global_model_estimation" , "Global Model Estimation" , envir=.dico)
  assign("txt_graphic_mean_sd" , "Graphic representation – mean and standard deviation" , envir=.dico)
  assign("txt_graphics" , "Graphics" , envir=.dico)
  assign("txt_graphics_informations" , "Information on graphics" , envir=.dico)
  assign("txt_group_analysis" , "Group Analysis" , envir=.dico)
  assign("txt_groups_analysis" , "group analysis" , envir=.dico)
  assign("txt_groups_variables" , " groups of Variables " , envir=.dico)
  assign("txt_grubbs_test" , " Grubbs Test" , envir=.dico)
  assign("txt_hierarchical_factorial_analysis" , "Hierarchical factor analysis" , envir=.dico)
  assign("txt_hierarchical_model_analysis" , "Hierarchical model analysis" , envir=.dico)
  assign("txt_hierarchical_models_complete_model_sig_at_each_step" , "Hierarchical models – significance of the complete model to each step" , envir=.dico)
  assign("txt_hierarchical_models_deviance_table" , " deviance table of the hierarchical models" , envir=.dico)
  assign("txt_hierarchical_models" , "hierarchical models" , envir=.dico)
  assign("txt_hierarchical_models_variance_analysis_table" , "variance analysis table of hierarchical models" , envir=.dico)
  assign("txt_hosmer_lemeshow_r_2" , "Hosmer and Lemeshow R^2" , envir=.dico)
  assign("txt_hypergeom_total_sample_fixed_rows_cols" , "hypergeometric test : Total fixed strength for rows and columns" , envir=.dico)
  assign("txt_hypothesis_analysis" , "Hypothesis tests Analysis " , envir=.dico)
  assign("txt_identified_outliers_synthesis" , "Summary of the number of observations identified as influential" , envir=.dico)
  assign("txt_identifying_outliers" , "Identification of influential values" , envir=.dico)
  assign("txt_id_variable" , " Identifier variable" , envir=.dico)
  assign("txt_import_data" , "import data" , envir=.dico)
  assign("txt_imput_missing_values" , "Imputation of Missing Values" , envir=.dico)
  assign("txt_independant_correlations" , "Independent correlations" , envir=.dico)
  assign("txt_independant_group_variables" , "Variables for independent groups" , envir=.dico)
  assign("txt_independant_variable" , "Independent variable" , envir=.dico)
  assign("txt_indepmulti_fixed_sample_rows_cols" , "indepMulti - Fixed number for columns (variable)" , envir=.dico)
  assign("txt_indepmulti_total_fixed_rows_cols" , "indepMulti - Total fixed strength for rows  (variable)" , envir=.dico)
  assign("txt_inferior" , "lower" , envir=.dico)
  assign("txt_inferior_or_equal_to" , "less than or equal to" , envir=.dico)
  assign("txt_inferior_proba" , "lower probability" , envir=.dico)
  assign("txt_inferior_to" , "less than" , envir=.dico)
  assign("txt_inflation_variance_factor" , "variance Inflation factor" , envir=.dico)
  assign("txt_influence_method" , "Influence Measurement method" , envir=.dico)
  assign("txt_information" , "Information" , envir=.dico)
  assign("txt_init_values" , "initial values" , envir=.dico)
  assign("txt_inspect_initial_values" , "Inspect initial values" , envir=.dico)
  assign("txt_inspect_model_matrices" , "Inspect model matrices" , envir=.dico)
  assign("txt_inspect_model_representation" , "Inspect model representation" , envir=.dico)
  assign("txt_interaction_effects" , "Interaction effects" , envir=.dico)
  assign("txt_interactive_model_variables" , "Interactive models variables" , envir=.dico)
  assign("txt_is_different_from" , "is different from" , envir=.dico)
  assign("txt_jointmulti_total_fixed_sample" , "jointMulti - Total fixed staff" , envir=.dico)
  assign("txt_judge1" , "Juge 1" , envir=.dico)
  assign("txt_judge2" , "Juge 2" , envir=.dico)
  assign("txt_kaiser_meyer_olkin_index" , "Kaiser-Meyer-Olkin overall index" , envir=.dico)
  assign("txt_keep_default_values" , "Keep default values " , envir=.dico)
  assign("txt_kendall_coeff" , "Kendall concordance Coefficient" , envir=.dico)
  assign("txt_kendall_partial_semipartial_tau" , "Kendall partial or semipartial tau" , envir=.dico)
  assign("txt_kendall_partial_tau" , "Kendall partial tau" , envir=.dico)
  assign("txt_kendall_semipartial_tau" , "Kendall Semi-Partial tau" , envir=.dico)
  assign("txt_kendall_tau" , "Kendall Tau" , envir=.dico)
  assign("txt_kolmogorov_smirnov_comparing_two_distrib" , "Kolmogorov-Smirnov test comparing two distributions" , envir=.dico)
  assign("txt_labeled_outliers" , "Values identified as influential" , envir=.dico)
  assign("txt_latent_variable_name" , "name of the latent variable " , envir=.dico)
  assign("txt_less_square_diagonally_pondered" , "mind square weight diagonally" , envir=.dico)
  assign("txt_less_square_generalized" , " generalized least squares" , envir=.dico)
  assign("txt_less_square_not_pondered" , "weighted least square" , envir=.dico)
  assign("txt_less_square_pondered" , "Weighted least square" , envir=.dico)
  assign("txt_levene_test_verifying_homogeneity_variances" , "Levene test verifying homogeneity of variance " , envir=.dico)
  assign("txt_likelihood_only_for_estimator" , "True (only for estimator=ML) [likelihood=default]" , envir=.dico)
  assign("txt_likelihood_ratio_g_test" , "likelihood ratio (G test)" , envir=.dico)
  assign("txt_lilliefors_d" , "Lilliefors D" , envir=.dico)
  assign("txt_linearity_graph_between_predictors_and_dependant_variable" , "Graph checking linearity between predictors and the dependent variable" , envir=.dico)
  assign("txt_link_only_for_estimator" , "Link function (only for estimator=MML) [link=probit]" , envir=.dico)
  assign("txt_list_of_objects_in_mem" , "list of objects in memory" , envir=.dico)
  assign("txt_logarithm" , "logarithm" , envir=.dico)
  assign("txt_long_or_large_format" , "Long or wide format" , envir=.dico)
  assign("txt_lower_bound_rmsea" , "lower bound of RMSEA" , envir=.dico)
  assign("txt_mann_whitney_test" , " Mann-Whitney test – Wilcoxon rank-sum test" , envir=.dico)
  assign("txt_mathematical_operations_on_variables" , "Mathematical operations on variables" , envir=.dico)
  assign("txt_matrix_type" , " type of matrix " , envir=.dico)
  assign("txt_max_likelihood_chi_squared_proba_value" , " probability value of the maximum likelihood chi-squared" , envir=.dico)
  assign("txt_max_likelihood" , "maximum likelihood" , envir=.dico)
  assign("txt_mcnemar_results_between_var_x" , "Results of McNemar test between variable" , envir=.dico)
  assign("txt_mcnemar_test" , "McNemar Test" , envir=.dico)
  assign("txt_mcnemar_test_with_continuity_correction" , "McNemar test with continuity correction" , envir=.dico)
  assign("txt_mcnemar_test_without_yates_correction" , "McNemar test without yates correction" , envir=.dico)
  assign("txt_mcnemar_test_with_yates_correction" , "McNemar Test with Yates Correction" , envir=.dico)
  assign("txt_mean1" , "Mean 1" , envir=.dico)
  assign("txt_mean2" , " Mean 2" , envir=.dico)
  assign("txt_mean_complexity" , "Average Complexity" , envir=.dico)
  assign("txt_mean_complexity_is" , "the average complexity is" , envir=.dico)
  assign("txt_means_adjusted_standard_errors" , "adjusted means and standard errors" , envir=.dico)
  assign("txt_means_comparison" , "Comparison of means " , envir=.dico)
  assign("txt_mean_sd_for_adjusted_data" , " Mean and standard deviation for adjusted data" , envir=.dico)
  assign("txt_mean_sd_for_non_adjusted_data" , " Mean and standard deviation for unadjusted data" , envir=.dico)
  assign("txt_mean_sd" , "Mean and standard deviation" , envir=.dico)
  assign("txt_measured_variable_name" , " name of the measured variable " , envir=.dico)
  assign("txt_median" , "Median" , envir=.dico)
  assign("txt_mediation_effect" , "Mediation Effect" , envir=.dico)
  assign("txt_mediator2" , "Mediator 2" , envir=.dico)
  assign("txt_mediator" , "Mediator" , envir=.dico)
  assign("txt_method_choice" , "Choice of the method" , envir=.dico)
  assign("txt_min_correlation_between_scores_and_factors" , "Minimum possible correlation between scores and factors" , envir=.dico)
  assign("txt_minus" , "Minus" , envir=.dico)
  assign("txt_missing_values_treatment" , "Treatment of Missing Values" , envir=.dico)
  assign("txt_mixt_correlations" , "mixed correlations" , envir=.dico)
  assign("txt_modalities_name_for" , "Names of the modalities for" , envir=.dico)
  assign("txt_modalities_to_regroup" , "Modalites to group" , envir=.dico)
  assign("txt_modality" , "modality" , envir=.dico)
  assign("txt_model_degrees_of_freedom" , "degrees of freedom model " , envir=.dico)
  assign("txt_model_matrix" , "Model Matrix" , envir=.dico)
  assign("txt_model_representation" , "Model representation" , envir=.dico)
  assign("txt_model_significance" , "Significance of the overall model" , envir=.dico)
  assign("txt_mosaic_plot" , "Mosaic plot" , envir=.dico)
  assign("txt_multicolinearity_tests" , "Multicolinearity tests" , envir=.dico)
  assign("txt_multicolinearity_test" , "Multicolinearity test" , envir=.dico)
  assign("txt_multiple_imputation_amelia" , "Multiple imputation - Amelia" , envir=.dico)
  assign("txt_multiple_r_square_of_factors_scores" , " multiple R-squared between scores and factors" , envir=.dico)
  assign("txt_multiplication" , "multiplication" , envir=.dico)
  assign("txt_multivariate_normality" , " multivariate Normality " , envir=.dico)
  assign("txt_nb_variables_measured" , "Number of measured variables " , envir=.dico)
  assign("txt_negative_values" , "negative values" , envir=.dico)
  assign("txt_new_data_set" , "new data set" , envir=.dico)
  assign("txt_new_dir" , "new directory" , envir=.dico)
  assign("txt_N_of_XY_corr" , "number of XY correlation " , envir=.dico)
  assign("txt_N_of_XY_NUM_corr" , " number of XY:TXT" , envir=.dico)
  assign("txt_N_of_XZ_corr" , " number of XZ correlation" , envir=.dico)
  assign("txt_N_of_XZ_NUM_corr" , " number of XZ:TXT" , envir=.dico)
  assign("txt_non_adjusted_data" , "Unadjusted data" , envir=.dico)
  assign("txt_non_centered" , "Non-centered" , envir=.dico)
  assign("txt_no" , "no" , envir=.dico)
  assign("txt_non_parametric_test" , "Non-parametric test" , envir=.dico)
  assign("txt_non_param_model" , "Non-parametric model" , envir=.dico)
  assign("txt_non_param_test" , "non-parametric test" , envir=.dico)
  assign("txt_non_pondered_coeff" , "unweighted Kappa coefficient " , envir=.dico)
  assign("txt_non_standardized_residuals" , "Non-standardized residuals" , envir=.dico)
  assign("txt_null_hypothesis_tests" , "null hypothesis tests" , envir=.dico)
  assign("txt_null_model_degrees_of_freedom" , "Degrees of freedom of the null model " , envir=.dico)
  assign("txt_numerator" , "Numerator" , envir=.dico)
  assign("txt_objective_function_of_model" , "objective function of the model " , envir=.dico)
  assign("txt_objective_function_of_null_model" , "objective function of the null model " , envir=.dico)
  assign("txt_objects_in_mem" , "objects in memory " , envir=.dico)
  assign("txt_object_to_remove" , "Objects to be deleted" , envir=.dico)
  assign("txt_observed" , "Observed values" , envir=.dico)
  assign("txt_observed_sample" , "Observed Effects" , envir=.dico)
  assign("txt_odd_ratio" , "Odds ratio" , envir=.dico)
  assign("txt_order" , "Sort" , envir=.dico)
  assign("txt_orthogonals_inverse" , "inverse orthogonal vectors" , envir=.dico)
  assign("txt_orthogonals" , "orthogonal vectors " , envir=.dico)
  assign("txt_other_correlations" , "Other correlations" , envir=.dico)
  assign("txt_other_data" , "other data" , envir=.dico)
  assign("txt_outliers" , "Influential observations" , envir=.dico)
  assign("txt_outliers_synthesis" , "Summary of influential observations" , envir=.dico)
  assign("txt_outliers_values" , "Influential values" , envir=.dico)
  assign("txt_packages_install" , " package installation " , envir=.dico)
  assign("txt_packages_update" , "packages update" , envir=.dico)
  assign("txt_packages_verification" , " package verification " , envir=.dico)
  assign("txt_parallel_analysis" , "parallel analysis" , envir=.dico)
  assign("txt_param_model" , "parametric model" , envir=.dico)
  assign("txt_param_tests" , "Parametric tests" , envir=.dico)
  assign("txt_param_test" , "parametric test" , envir=.dico)
  assign("txt_partial_and_semi_correlations" , "Partial and semi-partial correlations" , envir=.dico)
  assign("txt_partial_corr_BP_by_group" , "Partial Bravais-Pearson correlation by group" , envir=.dico)
  assign("txt_partial_correlations_matrix" , "Partial Correlations Matrix" , envir=.dico)
  assign("txt_partial_rho" , "partial Spearman rho" , envir=.dico)
  assign("txt_partial_semi_BP" , "Partial/semi-partial Bravais Pearson correlation" , envir=.dico)
  assign("txt_partial_semi_partial_rho" , "Partial/Semipartial Spearman Rho" , envir=.dico)
  assign("txt_partial_spearman_by_group" , "Partial Spearman correlation by group" , envir=.dico)
  assign("txt_participants_id" , "participant identifier" , envir=.dico)
  assign("txt_partila_correlations" , "Partial correlations" , envir=.dico)
  assign("txt_percentage_col" , "column percentage " , envir=.dico)
  assign("txt_percentage_row" , "Row percentage" , envir=.dico)
  assign("txt_percentage_total" , "Total percentage" , envir=.dico)
  assign("txt_percentile_bootstrap_on_m_estimators" , "Percentile bootstrap on M-estimetor" , envir=.dico)
  assign("txt_p_estimation_with_monter_carlo" , "P-value estimated by Monte Carlo simulation" , envir=.dico)
  assign("txt_plus" , "plus" , envir=.dico)
  assign("txt_poisson_total_not_fixed_sample" , "poisson - total non-fixed" , envir=.dico)
  assign("txt_polyc_correlations" , "polychoric correlations" , envir=.dico)
  assign("txt_polynomials" , "polynomials" , envir=.dico)
  assign("txt_pondered_kappa" , " weighted Kappa coefficient" , envir=.dico)
  assign("txt_positive_values" , "positive values" , envir=.dico)
  assign("txt_predicted_probabilities" , "predicted probability" , envir=.dico)
  assign("txt_predictor" , "Predictor" , envir=.dico)
  assign("txt_principal_analysis" , "Main Analysis" , envir=.dico)
  assign("txt_principal_analysis_using_psych_with_algo" , "principal component analysis using the [principal] function from the psych package, the algorithm is" , envir=.dico)
  assign("txt_principal_component_analysis" , "principal Component Analysis" , envir=.dico)
  assign("txt_probabilities" , "probabilities" , envir=.dico)
  assign("txt_probability_matrix" , "probability matrix" , envir=.dico)
  assign("txt_probability_value" , "probability value" , envir=.dico)
  assign("txt_proper_values_index" , "Index of eigenvalues" , envir=.dico)
  assign("txt_pseudo_r_square_delta" , "Delta of pseudo R-squared" , envir=.dico)
  assign("txt_p_value_with_monte_carlo" , "P-value  by Monte Carlo simulation" , envir=.dico)
  assign("txt_ranks_lower" , "ranks" , envir=.dico)
  assign("txt_ranks_upper" , " ranks" , envir=.dico)
  assign("txt_references" , "References" , envir=.dico)
  assign("txt_remove_object_in_memory" , "remove object from memory " , envir=.dico)
  assign("txt_replace_by_mean" , "Replace with mean" , envir=.dico)
  assign("txt_replace_by_median" , "Replace with median" , envir=.dico)
  assign("txt_residual_distribution" , "Distribution of residuals" , envir=.dico)
  assign("txt_residual_error" , "Residual error" , envir=.dico)
  assign("txt_residual" , "residual" , envir=.dico)
  assign("txt_residuals_distribution" , "Distribution of residuals" , envir=.dico)
  assign("txt_residue" , "Residuals" , envir=.dico)
  assign("txt_residues_significativity_holm_correction" , "Significance of residuals probability value corrected using Holm method" , envir=.dico)
  assign("txt_residue_standardized_adjusted" , " standardized ajusted residuals" , envir=.dico)
  assign("txt_residue_standardized" , " standardized residual " , envir=.dico)
  assign("txt_result" , "Result" , envir=.dico)
  assign("txt_rho" , "Spearman rho" , envir=.dico)
  assign("txt_robust_analysis" , "robust analysis" , envir=.dico)
  assign("txt_robusts" , "robust" , envir=.dico)
  assign("txt_robusts_statistics" , "robust statistics" , envir=.dico)
  assign("txt_robust_statistics" , " robust statistics - may take time" , envir=.dico)
  assign("txt_robusts_tests_with_bootstraps" , " robust test - with bootstrapping" , envir=.dico)
  assign("txt_rotation_is_a_rotation" , "rotation is a rotation" , envir=.dico)
  assign("txt_sample_size_NUM" , "Sample size:TXT" , envir=.dico)
  assign("txt_saturations_sum_of_squares" , "Sum of squares of saturations" , envir=.dico)
  assign("txt_search_for_new_function" , "Search for a new function" , envir=.dico)
  assign("txt_second_variables_set" , "Second set of variables" , envir=.dico)
  assign("txt_selected_data" , "data you just selected" , envir=.dico)
  assign("txt_selection_method_akaike" , "Selection method - Akaike information criterion" , envir=.dico)
  assign("txt_selection_method_bayesian_factor" , "Selection methods: Bayesian factors" , envir=.dico)
  assign("txt_selection_method" , "Selection method" , envir=.dico)
  assign("txt_selection_methods" , "Selection methods" , envir=.dico)
  assign("txt_selection" , "selection" , envir=.dico)
  assign("txt_select_obs" , "Select Observations" , envir=.dico)
  assign("txt_select_variables" , "Select variables" , envir=.dico)
  assign("txt_semi_BP" , "Semi-partial correction of Bravais Pearson" , envir=.dico)
  assign("txt_semicolon" , "semicolon" , envir=.dico)
  assign("txt_semi_partial_rho" , "Spearman Semi-Partial Rho" , envir=.dico)
  assign("txt_sequential_bayesian_factors_robustness_analysis" , "Sequential Bayesian Factors - Robustness Analysis" , envir=.dico)
  assign("txt_shapiro_wilk" , "Shapiro-Wilk W" , envir=.dico)
  assign("txt_simple_mediation_effect" , "simple mediation effect" , envir=.dico)
  assign("txt_slopes_homogeneity_between_groups_on_dependant_variable" , "Test of slopes homogeneite between groups on the dependent variable" , envir=.dico)
  assign("txt_spearman_kendall_corr_by_group" , "Spearman/Kendall correlation by group " , envir=.dico)
  assign("txt_specific_val_multiplication" , "multiplication by a specific value" , envir=.dico)
  assign("txt_specify_contrasts" , "specify contrasts" , envir=.dico)
  assign("txt_specify_model" , "Specify the model" , envir=.dico)
  assign("txt_specify_working_dir" , "specify working directory" , envir=.dico)
  assign("txt_spss_file" , "SPSS file" , envir=.dico)
  assign("txt_square" , "square" , envir=.dico)
  assign("txt_rectangular" , "rectangular" , envir=.dico)
  assign("txt_standardized_parameters" , "Standardized Parameters" , envir=.dico)
  assign("txt_statistic" , "statistic" , envir=.dico)
  assign("txt_step" , "step" , envir=.dico)
  assign("txt_student_bootstrap_on_truncated_means" , "Student bootstrap on truncated means" , envir=.dico)
  assign("txt_student_t_by_group" , "Student t-test by group" , envir=.dico)
  assign("txt_student_t_independant" , "student t-test for independent samples" , envir=.dico)
  assign("txt_student_t" , "Student t-test " , envir=.dico)
  assign("txt_student_t_test_norm" , "Student t-test - comparison to a norm" , envir=.dico)
  assign("txt_student_t_test_paired" , "Student t-test - comparison paired samples" , envir=.dico)
  assign("txt_substraction" , "subtraction" , envir=.dico)
  assign("txt_sufficient_factors" , "sufficient factors" , envir=.dico)
  assign("txt_superior_or_equal_to" , "greater than or equal to" , envir=.dico)
  assign("txt_superior_proba" , "high probability" , envir=.dico)
  assign("txt_superior" , " greater" , envir=.dico)
  assign("txt_superior_to" , " greater than" , envir=.dico)
  assign("txt_supports_alternative" , "support the alternative hypothesis" , envir=.dico)
  assign("txt_supports_null" , " support the null hypothesis" , envir=.dico)
  assign("txt_suppress_all_outliers" , "remove all outliers" , envir=.dico)
  assign("txt_suppress_outliers_manually" , "Manual outliers removal" , envir=.dico)
  assign("txt_synthesis_table" , "Summary Table" , envir=.dico)
  assign("txt_teaching_material" , "teaching material" , envir=.dico)
  assign("txt_tetra_polyc_corr_matrix_or_mixt" , "Tetrachoric/polychoric or mixed correlation matrix" , envir=.dico)
  assign("txt_this_tests_if" , "this tests whether" , envir=.dico)
  assign("txt_threshold" , "Threshold" , envir=.dico)
  assign("txt_time_1" , "time 1" , envir=.dico)
  assign("txt_time1" , "time1" , envir=.dico)
  assign("txt_time_2" , "time 2" , envir=.dico)
  assign("txt_time2" , "time2" , envir=.dico)
  assign("txt_tolerance" , "Tolerance" , envir=.dico)
  assign("txt_total_sample_not_fixed" , "Total sample non-fixed effect" , envir=.dico)
  assign("txt_troncature_num" , "Troncature:TXT" , envir=.dico)
  assign("txt_truncated_means" , "Truncated means" , envir=.dico)
  assign("txt_t_test_choice" , "t-test selection" , envir=.dico)
  assign("txt_tucker_lewis_fiability_factor" , "Tucker Lewis index - TLI" , envir=.dico)
  assign("txt_two_independant_samples" , "Two independent samples" , envir=.dico)
  assign("txt_two_paired_samples" , "Two paired samples" , envir=.dico)
  assign("txt_txt_file" , "Txt file" , envir=.dico)
  assign("txt_type" , "Type" , envir=.dico)
  assign("txt_understanding_alpha_and_power" , "Understanding Alpha and statistical Power" , envir=.dico)
  assign("txt_understanding_bayesian_inference" , "Understanding Bayesian Inference" , envir=.dico)
  assign("txt_understanding_central_limit_theorem" , "Understanding the central theorem limit" , envir=.dico)
  assign("txt_understanding_confidance_interval" , "Understanding the confidence interval" , envir=.dico)
  assign("txt_understanding_corr_2" , "Understanding correlation 2" , envir=.dico)
  assign("txt_understanding_corr" , "Understanding correlation" , envir=.dico)
  assign("txt_understanding_heterogenous_variance_effects" , "Understanding the effects of heterogeneous variances" , envir=.dico)
  assign("txt_understanding_likelihood" , "Understanding maximum likelihood" , envir=.dico)
  assign("txt_understanding_negative_positive_predic_power" , "Understanding positive and negative predictive power" , envir=.dico)
  assign("txt_understanding_prev_sens_specificity_2" , "Understanding Prevalence, Sensibility and Specificity 2" , envir=.dico)
  assign("txt_understanding_prev_sens_specificity" , "Understanding prevalence, sensitivity and specificity" , envir=.dico)
  assign("txt_upper_bound_rmsea" , " upper bound of RMSEA" , envir=.dico)
  assign("txt_user_exited_easieR" , "You exited easieR" , envir=.dico)
  assign("txt_values" , "values" , envir=.dico)
  assign("txt_value" , "value" , envir=.dico)
  assign("txt_variable_descriptive_statistics" , "Descriptive statistics of the variable " , envir=.dico)
  assign("txt_variables_coeff_matrix" , "variable coefficient matrix" , envir=.dico)
  assign("txt_variables_contribution_to_model" , "Contribution of variables to the model" , envir=.dico)
  assign("txt_variables_from_step" , "Variable at this step" , envir=.dico)
  assign("txt_verify_packages_install" , "Check package installation" , envir=.dico)
  assign("txt_view_data" , "view data" , envir=.dico)
  assign("txt_VIF" , "VIF" , envir=.dico)
  assign("txt_warning" , "Warning" , envir=.dico)
  assign("txt_wilcoxon_by_group" , "Wilcoxon by group" , envir=.dico)
  assign("txt_without_outliers" , "Data without influential value" , envir=.dico)
  assign("txt_without_welch_correction" , "without Welch correction" , envir=.dico)
  assign("txt_without_yates_correction" , "Without Yates Correction" , envir=.dico)
  assign("txt_with_welch_correction" , "with Welch correction" , envir=.dico)
  assign("txt_with_yates_correction" , "With Yates Correction" , envir=.dico)
  assign("txt_working_dir" , "Work Directory" , envir=.dico)
  assign("txt_x_axis_variables" , "Variable(s) on x-axis" , envir=.dico)
  assign("txt_XY_correlation" , "Correlation between X and Y" , envir=.dico)
  assign("txt_XY_NUM_correlation" , "Correlation between X and Y:TXT" , envir=.dico)
  assign("txt_XZ_correlation" , "Correlation between X and Z" , envir=.dico)
  assign("txt_XZ_NUM_correlation" , "Correlation between X and Z :TXT" , envir=.dico)
  assign("txt_y_axis_variables" , "Variable(s) on y-axis" , envir=.dico)
  assign("txt_yes" , "yes" , envir=.dico)
  assign("txt_your_data" , "Your data" , envir=.dico)
  assign("txt_YZ_correlation" , "Correlation between Y and Z" , envir=.dico)
  assign("txt_YZ_NUM_correlation" , "Correlation between Y and Z:TXT" , envir=.dico)
  assign("ask_probability_correction" , "Which p-value adjustment do you want ? If none, choose +none+" , envir=.dico)
  assign("ask_contrasts_must_be_ortho" , "Contrasts must be orthogonal. Do you want to continue?" , envir=.dico)
  assign("desc_bayesian_factors_chosen_in" , "Baysian factors is chosen in " , envir=.dico)
  assign("desc_cross_validation_issues" , "cross validation encountered some issues" , envir=.dico)
  assign("desc_easier_metapackage" , "easieR: An R metapackage. Retrieved from https://github.com/NicolasStefaniak/easieR" , envir=.dico)
  assign("desc_first_time_easier" , " If you are using easieR for the first time, please use the ez.install function to ensure that easieR works properly."  , envir=.dico)
  assign("ask_chose_variables" , "please choose the variable(s)" , envir=.dico)
  assign("ask_correlations_type" , "Type of correlations?" , envir=.dico)
  assign("ask_dependant_variable_name" , "What is the name of the dependent variable?" , envir=.dico)
  assign("ask_factors_number" , "How many factors?" , envir=.dico)
  assign("ask_filename" , "What name do you want to give the file?" , envir=.dico)
  assign("ask_independant_variable_name" , "What is the name of the independent variable?" , envir=.dico)
  assign("ask_is_long_format_correct" , "Is the long format structure of your data correct?" , envir=.dico)
  assign("ask_model" , "Model?" , envir=.dico)
  assign("ask_ordinal_variables" , "Ordinal Variables?" , envir=.dico)
  assign("ask_save_results" , "Save Results?" , envir=.dico)
  assign("ask_save" , "Do you want to save?" , envir=.dico)
  assign("ask_specify_contrasts" , "Please specify contrasts. " , envir=.dico)
  assign("ask_variables" , "Which variables do you want to select?" , envir=.dico)
  assign("ask_variables_type" , "Nature of the variables?" , envir=.dico)
  assign("ask_what_to_do" , "What do you want to do?" , envir=.dico)
  assign("ask_which_analysis" , "Which analysis do you want to perform?" , envir=.dico)
  assign("desc_all_contrasts_description" , "a priori contrasts testing hypothses defined beforehand. Pairwise contrasts allow comparing all pairs, with or without p-value adjustment." , envir=.dico)
  assign("desc_contrasts_must_be_coeff_matrices_in_list" , "Contracts must be coefficients matrices placed in a list where  each level name corresponds to a factor" , envir=.dico)
  assign("desc_percentage_outliers" , "percentage of observations considered influential" , envir=.dico)
  assign("desc_robusts_statistics_could_not_be_computed_verify_WRS" , "robust statistics could not be computed. Please verify the installation of the WRS package" , envir=.dico)
  assign("desc_some_participants_have_missing_values_on_repeated_measures" , "Some participants have missing values for repeted measures. They will be removed from the analyses." , envir=.dico)
  assign("txt_absence_of_difference_between_groups_test_on" , "Test for absence of difference between groups on " , envir=.dico)
  assign("txt_anova_on_medians" , "Anova on mediane" , envir=.dico)
  assign("txt_anova_on_m_estimator" , "ANOVA on M estimator" , envir=.dico)
  assign("txt_bayesian_factors" , "Baysian Factors" , envir=.dico)
  assign("txt_BP_correlation" , "Bravais-Pearson Correlation" , envir=.dico)
  assign("txt_center" , "center" , envir=.dico)
  assign("txt_cohen_d" , " Cohen D" , envir=.dico)
  assign("txt_correlations" , "Correlations" , envir=.dico)
  assign("txt_correlations_matrix" , "Correlations Matrix" , envir=.dico)
  assign("txt_descriptive_statistics_of_interaction_between_x" , "Descriptive statistics for the interaction between" , envir=.dico)
  assign("txt_descriptive_statistics" , "Descriptive statistics" , envir=.dico)
  assign("txt_empirical_chi_square_proba_value" , " probability value from empirical chi-square test" , envir=.dico)
  assign("txt_factor" , "factor." , envir=.dico)
  assign("txt_friedman_anova" , " Friedman Anova " , envir=.dico)
  assign("txt_import_results" , "import results" , envir=.dico)
  assign("txt_interface_objects_in_memory" , "Interface - objects in memory, memory clean, working directory, language" , envir=.dico)
  assign("txt_intraclass_correlation" , "Intraclass correlation" , envir=.dico)
  assign("txt_kruskal_wallis_pairwise" , "Kruskal-Wallis Test – Pairwise Comparison " , envir=.dico)
  assign("txt_kruskal_wallis_test" , "Kruskal-Wallis Test" , envir=.dico)
  assign("txt_latent_variables_intercept" , "Intercept of latent variables [int.lv.free=FALSE]" , envir=.dico)
  assign("txt_observed_variables_intercept" , "Intercept of observed variables [int.ov.free=FALSE]" , envir=.dico)
  assign("txt_logistic_regressions" , "Logistical Regressions" , envir=.dico)
  assign("txt_mauchly_test_sphericity_covariance_matrix" , "Mauchly test for sphericity of the covariance matrix" , envir=.dico)
  assign("txt_none" , "none" , envir=.dico)
  assign("txt_non_param_analysis" , "Nonparametric analysis" , envir=.dico)
  assign("txt_normality_tests" , "Normality test" , envir=.dico)
  assign("txt_pairwise_comparisons" , "Pairwise Comparations" , envir=.dico)
  assign("txt_pairwise" , "pairwise" , envir=.dico)
  assign("txt_partial_corr_BP" , "Partial Bravais-Pearson correlation" , envir=.dico)
  assign("txt_preprocess_sort_select_operations" , "Preprocessing (tri, selection, mathematical operations, Missing value processing)" , envir=.dico)
  assign("txt_press_enter_to_continue" , "Press [enter] to continue" , envir=.dico)
  assign("txt_regressions" , "regressions" , envir=.dico)
  assign("txt_repeated_measures" , " repeated Measures " , envir=.dico)
  assign("txt_sample_size" , " sample size " , envir=.dico)
  assign("txt_test_model" , " model Test " , envir=.dico)
  assign("txt_variables" , "variables" , envir=.dico)
  assign("txt_variable" , "variable" , envir=.dico)
  assign("desc_corr_group_analysis_spec" , "If you want to perform the analysis for different subsamples based on a categorical criterion (i.e.; perform a group analysis), choose yes. In this case, the analysis is performed both on the full sample and on the subsamples. If you want the analysis only the full sample, choose no. The group analysis is not available for robust statistics." , envir=.dico)
  assign("desc_outliers_removal_implications" , " removing all outliers Delete all values with p(chi.two)< 0.001. removing one observation at a time allows for a influential examination starting with the most extreme. The process stops when no more influential remain" , envir=.dico)
  assign("txt_bilateral" , "two-tailed" , envir=.dico)
  assign("desc_no_compatible_object_in_mem_for_aov" , "there is no object compatible with aov.plus in memory of R. You must first run an analysis of variance" , envir=.dico)
  assign("desc_this_function_means_and_sd_adjusted_interaction_effect_possible" , "This function provides the adjusted means and standard deviations as well as the corresponding graph. With post hoc selection on interactions, you can test pairwise interaction effects and simple effects. " , envir=.dico)
  assign("txt_anova_plus" , "Anova plus" , envir=.dico)
  assign("desc_center_and_center_reduce_explaination" , "Centering sets the mean to zero while maintaining the variable's scale. Standardization (z-score) sets the mean to 0 and the standard deviation to 1. Lower probability corresponds to P(z ≤ value), higher probability to P(z ≥ value). " , envir=.dico)
  assign("desc_proba_sum_is_not_one_or_not_enough_proba" , "The sum of probabilities is not equal to 1 or the number of probabilities does not match the number of variable levels. Please enter a valid probability vector" , envir=.dico)
  assign("desc_if_non_fixed_sample_poisson_law" , "If the total count is not fixed, observations are assumed to follow a poisson dustribution. The Distribution across factor levels is based on a fixed probability. This results in a Poisson distribution " , envir=.dico)
  assign("desc_distribution_is_joint_multinomial" , "The *Fixed total effect* option must be selected if the null hypothesis assumed that each cells in the table has a fixed count. This results in a multinomial distribution " , envir=.dico)
  assign("desc_distribution_is_independant_multinomial" , "The fixed total per row*option must be selected if each row has the same total, such as when matching across groups. This result is an multinomial independent distribution" , envir=.dico)
  assign("desc_corr_detailed_analysis" , "the correlation analysis includes descriptive statistics, normality tests, scatterplots, robust statistics, and all correlation coefficients. the correlation matrix helps control type 1 error and is suitable for a large number of correlations. the correlation comparison option allows for comparing two dependent or independent correlations \n The + other correlations + option includes tetrachoric and polychoric correlations" , envir=.dico)
  assign("desc_corr_values_must_be_between_min_1_and_1" , "The correlation values must be between -1 and 1. Count must be positive integers" , envir=.dico)
  assign("desc_you_can_choose_contrasts_you_want" , "You can choose the contrasts you want. Howerver, the rules for applying  contrasts must be followed. Contrasts can also be manually specified. In that case, please select them accordingly" , envir=.dico)
  assign("desc_square_matrix_rectangular_matrix" , "A square matrix includes all pairwise correlations between variables. A rectangular matrix correlates one set of variables with another distinct set." , envir=.dico)
  assign("desc_complete_dataset_vs_identification_outliers_vs_without_outliers" , "the complete data represent classical analysis on all usable data. Identifying influential values helps pinpoint observations that statistically impact results. analysis without influential values is conducted after removing these values. This option stores a cleaned dataset in R memory under the name *nettoyees*." , envir=.dico)
  assign("desc_welcome_in_easieR" , "Welcome to easieR - For more information, please visit:https://theeasierproject.wordpress.com/" , envir=.dico)
  assign("ask_variables_type_for_anova" , "Please specify the type(s) of variable(s) you want to include in the analysis.\nYou can select several (e.g., for mixed annova or ancova)" , envir=.dico)
  assign("ask_correction_anova_contrasts" , "apply Correction?" , envir=.dico)
  assign("txt_independant_groups" , "Independent groups" , envir=.dico)
  assign("txt_covariables" , "Covariates" , envir=.dico)
  assign("txt_cfa_information_default" , "information [information=default]" , envir=.dico)
  assign("txt_cfa_continuity_correction_zero_keep_margins_default" , "continuity correction [zero.keep.margins=default]" , envir=.dico)
  assign("txt_cfa_estimator_ml_default" , "estimator [estimator=ml]" , envir=.dico)
  assign("txt_cfa_groups_null_default" , "groups [group=NULL]" , envir=.dico)
  assign("txt_cfa_test_standard_default" , "test" , envir=.dico)
  assign("txt_cfa_standard_error_default" , "standard error" , envir=.dico)
  assign("txt_cfa_observed_variabes_standardization_true_default" , "standardization of observed variables" , envir=.dico)
  assign("txt_cfa_latent_variables_indicators_estimates_true_default" , "Estimation of latent variables indicators [std.lv=FALSE]" , envir=.dico)
  assign("desc_wls_corresponds_to_adf_plus_explaination_other_estimators" , "[WLS] corresponds to [ADF]. Estimators with extensions [M],[MV],[MVSF],[R] are robust versions of the classic estimators [MV],[WLS], [DWLS], [ULS]" , envir=.dico)
  assign("ask_observed_variables_intercept_zero" , "Intercept for observed variables =0?" , envir=.dico)
  assign("ask_latent_variables_intercept_zero" , "Intercept for latent variables =0?" , envir=.dico)
  assign("ask_how_to_treat_exaequo_rank" , "How do you want to handle ties? average* assigns the mean rank (most common), *first* assigns the first rank to the first occurrence, *last* to the last, *min* assigns the minimum rank to all ties *max* the maximum." , envir=.dico)
  assign("desc_for_ordinal_and_dicho_varible_prefer_min_res" , "For ordinal and dichomous variables, use the minimum residual method - minres - or weighted least squares - wls. For continuous variables, use maximum likelihood - ml if assumption of normality is met " , envir=.dico)
  assign("desc_saturation_criterion_show_only_above_threshold" , "The saturation criterion allows displaying only the saturation above the defined threshold in the results table" , envir=.dico)
  assign("desc_to_find_new_analysis_search_in_english" , "To find a new analysis search using English terms. You may enter multiple keywords. An html page listing all related packages will open." , envir=.dico)
  assign("txt_division" , "division" , envir=.dico)
  assign("desc_if_you_select_both_operations_value_will_be_added_to_chose_cols" , "If you select both options, the specified value will be added to all the selected columns, then the selected columns will be summed. To add a specific value only to the total, select the column addition option alone." , envir=.dico)
  assign("desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols" , "If you select both options, the specified value will be multiplied with all the selected columns, then the selected columns will be multiplied together. To multiply a specific value only to the total, please select the column multipication option alone." , envir=.dico)
  assign("ask_chose_values_on_left_of_minus_symbol" , "Please select the values to the left of the *minus*symbol. If multiple variables are selected, matrix calculation rules will apply. " , envir=.dico)
  assign("desc_one_or_same_number_cols_on_both_sides_only" , "There must be either one column or the same number of columns on both sides of the *less* symbol." , envir=.dico)
  assign("ask_specify_exponant_value" , "Please specify the exponent value. NOTE: For roots, the exponent is the inverse. For example, the square root is 1/2, the cube root 1/3..." , envir=.dico)
  assign("desc_expression_must_be_correct_example" , "The expression must be valid. You can use variables name directly. the allowed operators are +,-,*,/,^,(,). A correct expression would be, for example: " , envir=.dico)
  assign("ask_chose_relation_between_vars_regressions_log" , "Please choose the type(s) of relationships between variables. Additive effects : y=X1+X2. interaction effects : Y=X1+X2+X1:X2" , envir=.dico)
  assign("ask_variables_order_for_max_likelihood" , "The order of variables entry is important for maximum likelihood. Please specify the entry order of the variables" , envir=.dico)
  assign("ask_integrate_probabilities_to_dataset" , "Do you want to integrate the probabilities into your dataset?" , envir=.dico)
  assign("ask_specify_other_options_regressions" , "Do you want to specify additionnal options? selection methods help choose the best model using statistical criterion. Hierarchical models allow comparing multiple models. Cross validations tests whether a model depends on the dataset. This option is to be used with selection methods. group analysis runs the same regression on subgroups. Influence diagnostics help identify influential values. " , envir=.dico)
  assign("desc_possible_apply_multiple_selection_criterion" , "It is possible to apply multiple selection criteria simultaneously, whether or not involving several variables. Please specify the number of to wich the selection criteria should apply, then choose the relevant variables" , envir=.dico)
  assign("desc_skew_and_kurtosis_between_1_and_3" , "the skewness and kurtosis value, should be between 1 and 3:TXT" , envir=.dico)
  assign("desc_with_two_equal_means_ratio_must_be_5_percent" , "With two equal or nearly equal means, the error rate must be 5%. Gradually modify the difference between standard deviations to observe how the alpha error rate changes" , envir=.dico)
  assign("desc_bilateral_superior_inferior_test_t" , "a Bilateral tests checks whether a difference exists. Superior option tests if the mean is strictly greater \n The inferior option tests if the mean is strictly smaller" , envir=.dico)
  assign("txt_numeric_variables" , "Numeric variables" , envir=.dico)
  assign("txt_select_language" , "select language" , envir=.dico)
  assign("txt_dot_adjusted" , ".adjusted" , envir=.dico)
  assign("txt_bca_inferior_limit" , "Bca lower limit" , envir=.dico)
  assign("txt_bca_inferior_limit" , "Bca lower limi" , envir=.dico)
  assign("txt_bca_superior_limit" , " Bca upper limi" , envir=.dico)
  assign("txt_bca_superior_limit" , "Bca upper limi" , envir=.dico)
  assign("txt_bca_superior_limit" , "Bca upper limi " , envir=.dico)
  assign("txt_centered_dot_reduced" , "centered.reduced" , envir=.dico)
  assign("txt_chi_dot_squared" , "chi-squared" , envir=.dico)
  assign("txt_chi_dot_squared_model" , "chi-squared model" , envir=.dico)
  assign("txt_chi_dot_squared" , "chi.squared" , envir=.dico)
  assign("txt_chi_dot_squared" , "chi-squared" , envir=.dico)
  assign("txt_chi_dot_squared_adjustment" , "chi- squared adjustment" , envir=.dico)
  assign("txt_pairwise_comparison" , "pairwaise comparison" , envir=.dico)
  assign("txt_continuous" , "continuous" , envir=.dico)
  assign("txt_greenhouse_geisser_huynn_feldt_correction" , "Correction : Greenhouse-Geisser &  Hyunh-Feldt" , envir=.dico)
  assign("txt_df" , "df" , envir=.dico)
  assign("txt_df1" , "df1" , envir=.dico)
  assign("txt_df_parenthesis_1" , "Df(1)" , envir=.dico)
  assign("txt_df2" , "df2" , envir=.dico)
  assign("txt_df_parenthesis_2" , "Df(2)" , envir=.dico)
  assign("txt_df_denom" , "df.denom" , envir=.dico)
  assign("txt_df_parenthesis_denom" , "Df (denom)" , envir=.dico)
  assign("txt_df_effect" , "df.effet" , envir=.dico)
  assign("txt_df_num" , "df.num" , envir=.dico)
  assign("txt_df_parenthesis_num" , "Df (num)" , envir=.dico)
  assign("txt_df_predictor" , "df predictor" , envir=.dico)
  assign("txt_df_residual" , "df.residual" , envir=.dico)
  assign("txt_df_residuals" , "df.residuals" , envir=.dico)
  assign("txt_delta_r_squared" , "Delta R. squared" , envir=.dico)
  assign("txt_error" , "Error" , envir=.dico)
  assign("txt_error_BP" , "Error.BP" , envir=.dico)
  assign("txt_error_spearman" , "Error.Spearman" , envir=.dico)
  assign("txt_error_dot_standard_short" , "error.st" , envir=.dico)
  assign("txt_error_dot_standard" , "error.standard" , envir=.dico)
  assign("txt_error_dot_standard" , "Error.standard" , envir=.dico)
  assign("txt_space" , "space" , envir=.dico)
  assign("txt_estimator" , "estimator" , envir=.dico)
  assign("txt_global_model_estimate" , "Global model estimation" , envir=.dico)
  assign("txt_hf_p_value" , "HF.p.value" , envir=.dico)
  assign("txt_ci_inferior" , "CI lower" , envir=.dico)
  assign("txt_ci_inferior_limit" , "CI lower limit" , envir=.dico)
  assign("txt_ci_superior_limit" , "CI upper limit " , envir=.dico)
  assign("txt_ci_superior" , "CI upper " , envir=.dico)
  assign("txt_large" , "large" , envir=.dico)
  assign("txt_large_half" , "large - 0.5" , envir=.dico)
  assign("txt_inferior_limit" , " lower limit " , envir=.dico)
  assign("txt_ci_inferior_limit_dot" , " lower limit CI" , envir=.dico)
  assign("txt_ci_superior_limit" , " upper limit " , envir=.dico)
  assign("txt_ci_superior_limit_dot" , " upper limit.CI" , envir=.dico)
  assign("txt_ci_superior_limit_dot" , " upper limit.CI" , envir=.dico)
  assign("txt_r_squared_matrix" , "matrix r.squared" , envir=.dico)
  assign("txt_truncated_m" , "M.truncated" , envir=.dico)
  assign("txt_multiplied_by" , "multiplied.by" , envir=.dico)
  assign("txt_dot_cleaned" , ".cleaned" , envir=.dico)
  assign("txt_cleaned" , "cleaned" , envir=.dico)
  assign("txt_bootstrap_dot_number" , "Number.bootstraps" , envir=.dico)
  assign("txt_odd_ratio_dot" , "Odd.ratio" , envir=.dico)
  assign("desc_install_bad_packages" , "Package.incorrectly.installed" , envir=.dico)
  assign("desc_install_correct_packages" , "packages. correctly.installed " , envir=.dico)
  assign("txt_critical_p_corrected" , "critical.p.corrected" , envir=.dico)
  assign("txt_percentile_inferior_limit_dot" , "Percentile.lower limit" , envir=.dico)
  assign("txt_percentile_superior_limit_dot" , "Percentile. upper limit " , envir=.dico)
  assign("txt_percentage_removed_obs" , "Percentage.obs.removed" , envir=.dico)
  assign("txt_percent_removed_obs" , "Percent.obs.removed" , envir=.dico)
  assign("txt_r_dot_square" , "r.square" , envir=.dico)
  assign("txt_r_square" , "R square" , envir=.dico)
  assign("txt_r_dot_square" , "R.square" , envir=.dico)
  assign("txt_r_dot_two" , "r.two" , envir=.dico)
  assign("txt_r_dot_two" , "R.two" , envir=.dico)
  assign("txt_r_dot_two_adjusted" , "R.two.aj" , envir=.dico)
  assign("txt_log_regression_dot" , "Regressions.logistic" , envir=.dico)
  assign("txt_multiple_regressions_dot" , "regressions.multiples" , envir=.dico)
  assign("txt_multiple_regressions_dot" , "Regressions.multiples" , envir=.dico)
  assign("txt_rho_dot_square" , "rho.squared" , envir=.dico)
  assign("txt_critical_dot_threshold" , "critical.threshold" , envir=.dico)
  assign("txt_critical_dot_threshold" , " critical.threshold" , envir=.dico)
  assign("txt_spearman_df" , "Spearman.df" , envir=.dico)
  assign("txt_specificity" , "specifity" , envir=.dico)
  assign("txt_ultrawide" , "ultrawide" , envir=.dico)
  assign("txt_ultrawide" , "ultrawide" , envir=.dico)
  assign("txt_ultrawide_val" , "ultra wide - 0.707" , envir=.dico)
  assign("txt_absolute_dot_val" , "value.absolute." , envir=.dico)
  assign("txt_contrast_dot_val" , "Value.contrast" , envir=.dico)
  assign("txt_critical_dot_val" , "Value.critical" , envir=.dico)
  assign("txt_p_dot_val" , "p.value" , envir=.dico)
  assign("txt_p_dot_val_lilliefors" , "p.value Lilliefors" , envir=.dico)
  assign("txt_p_dot_val_sw" , "p.value Shapiro-Wilk" , envir=.dico)
  assign("txt_test_dot_val" , "test.value" , envir=.dico)
  assign("txt_z_dot_val" , "Z.value" , envir=.dico)
  assign("txt_value" , "value" , envir=.dico)
  assign("txt_vector_length_zero" , "vector of length zero" , envir=.dico)
  assign("txt_kendall_w" , "Kendall.W" , envir=.dico)
  assign("txt_synthesis" , "Synthesis" , envir=.dico)
  assign("txt_truncated_mean_0_2" , "Test on truncated mean 0.2" , envir=.dico)
  assign("txt_cramer_v_square" , "V.square" , envir=.dico)
  assign("txt_effect_size_dot" , "Effect.size" , envir=.dico)
  assign("txt_gg_p_value" , "GG.p.value" , envir=.dico)
  assign("txt_var_explained_dot" , "Var.explained" , envir=.dico)
  assign("txt_V_sq_" , "V.squared" , envir=.dico)
  assign("desc_bootstrap_percentile_anova" , "Trimmed mean anova using  a percentile-t bootstrap method" , envir=.dico)
  assign("txt_effect_size_dot_inf" , "effect size ci lower" , envir=.dico)
  assign("txt_effect_size_dot_sup" , "effect size ci upper" , envir=.dico)
  assign("RML_and_within_not_allowed" , "If RML is not null, within must be null" , envir=.dico) 
  assign("txt_bp1" , "Mardia’s estimate of multivariate skew", envir=.dico) 
  assign("txt_bp2" , "Mardia’s  estimate of multivariate kurtosis", envir=.dico) 
  assign("txt_small" , "Mardia s small sample skew statistic" , envir=.dico) 
  assign("txt_Mardia_statistic" , "Mardia s statistic", envir=.dico) 
}